Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons (2312.17612v3)
Abstract: Printed Electronics (PE) feature distinct and remarkable characteristics that make them a prominent technology for achieving true ubiquitous computing. This is particularly relevant in application domains that require conformal and ultra-low cost solutions, which have experienced limited penetration of computing until now. Unlike silicon-based technologies, PE offer unparalleled features such as non-recurring engineering costs, ultra-low manufacturing cost, and on-demand fabrication of conformal, flexible, non-toxic, and stretchable hardware. However, PE face certain limitations due to their large feature sizes, that impede the realization of complex circuits, such as machine learning classifiers. In this work, we address these limitations by leveraging the principles of Approximate Computing and Bespoke (fully-customized) design. We propose an automated framework for designing ultra-low power Multilayer Perceptron (MLP) classifiers which employs, for the first time, a holistic approach to approximate all functions of the MLP's neurons: multiplication, accumulation, and activation. Through comprehensive evaluation across various MLPs of varying size, our framework demonstrates the ability to enable battery-powered operation of even the most intricate MLP architecture examined, significantly surpassing the current state of the art.
- J. Isohanni, “Use of functional ink in a smart tag for fast-moving consumer goods industry,” Springer Journal of Packaging Technology and Research, vol. 6, pp. 187–198, 2022.
- N. Bleier, M. Mubarik, F. Rasheed, J. Aghassi-Hagmann, M. B. Tahoori, and R. Kumar, “Printed microprocessors,” in Annu. Int. Symp. Computer Architecture (ISCA), jun 2020, pp. 213–226.
- P. Lacy, J. Long, and W. Spindler, “Fast-moving consumer goods (fmcg) industry profile,” in The Circular Economy Handbook. Springer, 2020.
- J. S. Chang, A. F. Facchetti, and R. Reuss, “A circuits and systems perspective of organic/printed electronics: Review, challenges, and contemporary and emerging design approaches,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 7, no. 1, pp. 7–26, 2017.
- G. Cadilha Marques et al., “Digital power and performance analysis of inkjet printed ring oscillators based on electrolyte-gated oxide electronics,” Applied Physics Letters, vol. 111, no. 10, p. 102103, 2017.
- T. Lei et al., “Low-voltage high-performance flexible digital and analog circuits based on ultrahigh-purity semiconducting carbon nanotubes,” Nature communications, vol. 10, no. 1, p. 2161, 2019.
- G. Armeniakos, G. Zervakis, D. Soudris, M. B. Tahoori, and J. Henkel, “Co-design of approximate multilayer perceptron for ultra-resource constrained printed circuits,” IEEE Transactions on Computers, pp. 1–8, 2023.
- M. H. Mubarik et al., “Printed machine learning classifiers,” in Annu. Int. Symp. Microarchitecture (MICRO), 2020, pp. 73–87.
- G. Armeniakos, G. Zervakis, D. Soudris, M. B. Tahoori, and J. Henkel, “Cross-layer approximation for printed machine learning circuits,” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022, pp. 190–195.
- G. Armeniakos, G. Zervakis, D. Soudris, M. B. Tahoori, and J. Henkel, “Model-to-circuit cross-approximation for printed machine learning classifiers,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1–1, 2023.
- A. Kokkinis et al., “Hardware-aware automated neural minimization for printed multilayer perceptrons,” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2023.
- G. Armeniakos, G. Zervakis, D. Soudris, and J. Henkel, “Hardware approximate techniques for deep neural network accelerators: A survey,” ACM Comput. Surv., vol. 55, no. 4, nov 2022. [Online]. Available: https://doi.org/10.1145/3527156
- J. Henkel et al., “Approximate computing and the efficient machine learning expedition,” in International Conference On Computer Aided Design (ICCAD), 2022, pp. 1–9.
- D. D. Weller et al., “Printed stochastic computing neural networks,” in Design, Automation Test in Europe Conference Exhibition (DATE), 2021, pp. 914–919.
- J. S. Chang, A. F. Facchetti, and R. Reuss, “A circuits and systems perspective of organic/printed electronics: review, challenges, and contemporary and emerging design approaches,” IEEE Journal on emerging and selected topics in circuits and systems, vol. 7, no. 1, pp. 7–26, 2017.
- E. Özer et al., “A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate,” Nature Electronics, vol. 3, pp. 1–7, 07 2020.
- D. D. Weller, M. Hefenbrock, M. B. Tahoori, J. Aghassi-Hagmann, and M. Beigl, “Programmable neuromorphic circuit based on printed electrolyte-gated transistors,” in 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), 2020, pp. 446–451.
- J. Biggs et al., “A natively flexible 32-bit arm microprocessor,” Nature, vol. 595, pp. 532–536, 2021.
- K. Iordanou et al., “Tiny classifier circuits: Evolving accelerators for tabular data,” arXiv:2303.00031, 2023.
- C. Sung et al., “Mix and match: A novel fpga-centric deep neural network quantization framework,” in IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2021.
- Y. Hanchen, Z. Xiaofan, H. Zhize, C. Gengsheng, and D. Chen, “Hybriddnn: A framework for high-performance hybrid dnn accelerator design and implementation,” in 57th ACM/IEEE Design Automation Conference (DAC), 2020.
- J. Meng, S. K. Venkataramanaiah, C. Zhou, P. Hansen, P. Whatmough, and J.-s. Seo, “Fixyfpga: Efficient fpga accelerator for deep neural networks with high element-wise sparsity and without external memory access,” in International Conference on Field-Programmable Logic and Applications (FPL), 2021, pp. 9–16.
- K. Balaskas, G. Zervakis, K. Siozios, M. B. Tahoori, and J. Henkel, “Approximate decision trees for machine learning classification on tiny printed circuits,” in Int. Symp. Quality Electronic Design, 2022, pp. 1–6.
- D. Dua and C. Graff, “UCI machine learning repository,” 2017.
- C. Coelho et al., “Ultra low-latency, low-area inference accelerators using heterogeneous deep quantization with qkeras and hls4ml,” arXiv:2006.10159, 2021.
- H. W. Kuhn, “The hungarian method for the assignment problem,” Naval Research Logistics Quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.
- H. Jiang, F. J. H. Santiago, H. Mo, L. Liu, and J. Han, “Approximate arithmetic circuits: A survey, characterization, and recent applications,” Proceedings of the IEEE, vol. 108, no. 12, pp. 2108–2135, 2020.
- D. Kalyanmoy, P. Amrit, A. Sameer, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE Trans. Evol. Comp., vol. 6, no. 2, pp. 182–197, 2002.
- C. Marques et al., “Progress Report on “From Printed Electrolyte-Gated Metal-Oxide Devices to Circuits”,” Advanced Materials, vol. 31, 2019.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.