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Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks (2402.16628v1)

Published 26 Feb 2024 in cs.NE and cond-mat.mes-hall

Abstract: Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or "learning-to-learn". The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.

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References (54)
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Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces GluR1-dependent associative short-term potentiation: A potential mechanism for short-term memory. Journal of Cognitive Neuroscience 22, 2530–2540 (2010). [3] Zucker, R. S. & Regehr, W. G. Short-Term Synaptic Plasticity. Annual Review of Physiology 64, 355–405 (2002). URL https://doi.org/10.1146/annurev.physiol.64.092501.114547. [4] Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zucker, R. S. & Regehr, W. G. Short-Term Synaptic Plasticity. Annual Review of Physiology 64, 355–405 (2002). URL https://doi.org/10.1146/annurev.physiol.64.092501.114547. [4] Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. 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Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. 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Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. 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(eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
  2. A single brief burst induces GluR1-dependent associative short-term potentiation: A potential mechanism for short-term memory. Journal of Cognitive Neuroscience 22, 2530–2540 (2010). [3] Zucker, R. S. & Regehr, W. G. Short-Term Synaptic Plasticity. Annual Review of Physiology 64, 355–405 (2002). URL https://doi.org/10.1146/annurev.physiol.64.092501.114547. [4] Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zucker, R. S. & Regehr, W. G. Short-Term Synaptic Plasticity. Annual Review of Physiology 64, 355–405 (2002). URL https://doi.org/10.1146/annurev.physiol.64.092501.114547. [4] Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). 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Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. 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[29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. 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Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. 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[44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. 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Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Wang, Y. et al. Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature neuroscience 9, 534–542 (2006). [5] Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). 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Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. 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URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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[38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. 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(eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. 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Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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[10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). 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(eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Erickson, M. A., Maramara, L. A. & Lisman, J. A single brief burst induces glur1-dependent associative short-term potentiation: a potential mechanism for short-term memory. Journal of cognitive neuroscience 22, 2530–2540 (2010). [6] Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). 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Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. 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URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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[38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. 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(eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. 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Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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[11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. 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URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Abraham, W. C. & Bear, M. F. Metaplasticity: the plasticity of synaptic plasticity. Trends in neurosciences 19, 126–130 (1996). [7] Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). 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[15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. 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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. 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Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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[31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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(eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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[24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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[41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Barrett, A. B., Billings, G. O., Morris, R. G. & Van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Computational Biology 5 (2009). [8] Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). 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[15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. 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[27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. 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URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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[29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. 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Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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(eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). 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Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. 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[47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). 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Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). 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Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks, 1126–1135 (2017). [9] Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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[15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. 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Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. 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Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. 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URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. 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[14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Stanley, K. & Clune, J. Differentiable plasticity: training plastic neural networks with backpropagation, 3559–3568 (2018). [10] Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Miconi, T., Rawal, A., Clune, J. & Stanley, K. O. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity. arXiv preprint arXiv:2002.10585 (2020). [11] Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Tyulmankov, D., Yang, G. R. & Abbott, L. F. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron 110, 544–557 (2022). URL https://doi.org/10.1016/j.neuron.2021.11.009. [12] Najarro, E. & Risi, S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems 2020-Decem (2020). [13] Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. 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IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. 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Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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[16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). 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[24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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[15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. 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Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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[47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. 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(eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) 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Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. 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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. 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An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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[19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 5149–5169 (2022). [14] Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Nadim, F. & Manor, Y. The role of short-term synaptic dynamics in motor control. Current opinion in neurobiology 10, 683–690 (2000). [15] Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. 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Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. 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Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. 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Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. 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Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. 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Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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(eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Citri, A. & Malenka, R. C. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41 (2008). [16] Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Shimizu, G., Yoshida, K., Kasai, H. & Toyoizumi, T. Computational roles of intrinsic synaptic dynamics. Current Opinion in Neurobiology 70, 34–42 (2021). [17] Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. 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Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). 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URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. 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Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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(eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Zador, A. et al. Catalyzing next-generation artificial intelligence through neuroai. Nature communications 14, 1597 (2023). [18] Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Canziani, A., Paszke, A. & Culurciello, E. An Analysis of Deep Neural Network Models for Practical Applications 1–7 (2016). URL http://arxiv.org/abs/1605.07678. [19] Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. 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(eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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(eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. 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Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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[21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. 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(eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Patterson, D. et al. Carbon Emissions and Large Neural Network Training 1–22 (2021). URL http://arxiv.org/abs/2104.10350. [20] Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Moraitis, T., Sebastian, A. & Eleftheriou, E. Short-term synaptic plasticity optimally models continuous environments (2020). URL http://arxiv.org/abs/2009.06808. [21] Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. 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Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). 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(eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. 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[44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Rodriguez, H. G., Guo, Q. & Moraitis, T. Chaudhuri, K. et al. (eds) Short-term plasticity neurons learning to learn and forget. (eds Chaudhuri, K. et al.) Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, 18704–18722 (2022). URL https://proceedings.mlr.press/v162/rodriguez22b.html. [22] Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xu, X. et al. Scaling for edge inference of deep neural networks. Nature Electronics 1, 216–222 (2018). [23] Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yu, S. Neuro-Inspired Computing with Emerging Nonvolatile Memorys. Proceedings of the IEEE 106, 260–285 (2018). [24] Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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[47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. 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Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E. Memory devices and applications for in-memory computing. Nature Nanotechnology 15, 529–544 (2020). URL http://dx.doi.org/10.1038/s41565-020-0655-z. [25] Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). 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[31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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(eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Jo, S. H. et al. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters (2010). URL https://pubs.acs.org/sharingguidelines. [26] Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Waser, R. Nanoelectronics and Information Technology (John Wiley and Sons, 2012). [27] Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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(eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. 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Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. 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URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. 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Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Emboras, A. et al. Opto-electronic memristors: Prospects and challenges in neuromorphic computing. Applied Physics Letters 117 (2020). [28] Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Portner, K. et al. Analog Nanoscale Electro-Optical Synapses for Neuromorphic Computing Applications. ACS Nano 15, 14776–14785 (2021). [29] Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? 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Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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[54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. 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URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). 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Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing. Nature Reviews Materials 7, 575–591 (2022). [30] Demirag, Y. et al. PCM-trace: Scalable synaptic eligibility traces with resistivity drift of phase-change materials. Proceedings - IEEE International Symposium on Circuits and Systems 2021-May (2021). [31] Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. 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Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. 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[36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. 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(eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). 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[46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. 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Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. 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Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. 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[44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. 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Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R., Huang, H. M. & Guo, X. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
  31. Memristive Synapses and Neurons for Bioinspired Computing. Advanced Electronic Materials 5, 1–32 (2019). [32] Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Choi, S., Yang, J. & Wang, G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. Advanced Materials 32, 1–26 (2020). [33] Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Yang, R. et al. Synaptic Suppression Triplet-STDP Learning Rule Realized in Second-Order Memristors. Advanced Functional Materials 28, 1–10 (2018). [34] Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. 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Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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(eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) 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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. 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Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. 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Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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[49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). 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Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Xiong, J. et al. Bienenstock, Cooper, and Munro Learning Rules Realized in Second-Order Memristors with Tunable Forgetting Rate. Advanced Functional Materials 29, 1–8 (2019). [35] Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. 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URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. 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Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). 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Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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[39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Pfeiffer, M. & Pfeil, T. Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience 12 (2018). [36] Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Sarwat, S. G., Kersting, B., Moraitis, T., Jonnalagadda, V. P. & Sebastian, A. Phase-change memtransistive synapses for mixed-plasticity neural computations. Nature Nanotechnology (2022). URL https://doi.org/10.1038/s41565-022-01095-3. [37] Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Advanced Materials 32, 2003984 (2020). URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202003984. [38] Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Cruz-Albrecht, J. M., Yung, M. W. & Srinivasa, N. Energy-efficient neuron, synapse and STDP integrated circuits. IEEE Transactions on Biomedical Circuits and Systems 6, 246–256 (2012). [39] Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Joubert, A., Belhadj, B., Temam, O. & Héliot, R. Hardware spiking neurons design: Analog or digital? Proceedings of the International Joint Conference on Neural Networks 6–10 (2012). [40] Gopalakrishnan, R. & Basu, A. Triplet Spike Time Dependent Plasticity: A floating-gate Implementation 1–13 (2015). URL http://arxiv.org/abs/1512.00961. [41] Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. Coexistence of filamentary and homogeneous resistive switching in Fe-doped SrTiO3 thin-film memristive devices. Advanced Materials 22, 4819–4822 (2010). [42] Baeumer, C. et al. Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. 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Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes. Nature Communications 7, 1–7 (2016). [43] Cooper, D. et al. Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). 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Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Revealed by In Situ TEM. Advanced Materials 29, 1–8 (2017). [44] Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. 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URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Muenstermann, R., Menke, T., Dittmann, R. & Waser, R. 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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Menzel, S. & Waser, R. in Mechanism of memristive switching in OxRAM 137–170 (2019). [45] Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. 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Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) 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[48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. 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URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. 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Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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(eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Siegel, S. et al. Trade-Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices. Advanced Electronic Materials 7 (2021). [46] Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Mikheev, E., Hoskins, B. D., Strukov, D. B. & Stemmer, S. Resistive switching and its suppression in Pt/Nb:SrTiO3 junctions. Nature Communications 5 (2014). [47] Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nature Machine Intelligence 1, 133–143 (2019). URL https://doi.org/10.1038/s42256-019-0025-4. [48] Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. Applied Physics Letters 93 (2008). [50] Liang, E. et al. Dy, J. & Krause, A. (eds) RLlib: Abstractions for distributed reinforcement learning. (eds Dy, J. & Krause, A.) Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, 3053–3062 (2018). URL https://proceedings.mlr.press/v80/liang18b.html. [51] Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Li, Y. et al. Nanoscale Chemical and Valence Evolution at the Metal/Oxide Interface: A Case Study of Ti/SrTiO3. Advanced Materials Interfaces 3, 1–8 (2016). [49] La Mattina, F., Bednorz, J. G., Alvarado, S. F., Shengelaya, A. & Keller, H. Detection of charge transfer processes in Cr-doped SrTi O3 single crystals. 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  51. Mnih, V. et al. Balcan, M. F. & Weinberger, K. Q. (eds) Asynchronous methods for deep reinforcement learning. (eds Balcan, M. F. & Weinberger, K. Q.) Proceedings of The 33rd International Conference on Machine Learning, Vol. 48 of Proceedings of Machine Learning Research, 1928–1937 (2016). URL https://proceedings.mlr.press/v48/mniha16.html. [52] Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Belgaid, M. c., Rouvoy, R. & Seinturier, L. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
  52. Pyjoules: Python library that measures python code snippets (2019). URL https://github.com/powerapi-ng/pyJoules. [53] Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
  53. Dally, B. The path to exascale computing (2015). URL https://images.nvidia.com/events/sc15/pdfs/SC5102-path-exascale-computing.pdf. [54] Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805. Bhalachandra, S., Austin, B., Williams, S. & Wright, N. J. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
  54. Understanding the impact of input entropy on fpu, cpu, and gpu power (2022). 2212.08805.
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Authors (9)
  1. Christoph Weilenmann (1 paper)
  2. Alexandros Ziogas (1 paper)
  3. Till Zellweger (1 paper)
  4. Kevin Portner (2 papers)
  5. Marko Mladenović (7 papers)
  6. Manasa Kaniselvan (5 papers)
  7. Timoleon Moraitis (12 papers)
  8. Mathieu Luisier (62 papers)
  9. Alexandros Emboras (5 papers)
Citations (2)
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