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Data-driven Software-based Power Estimation for Embedded Devices (2407.02764v1)

Published 3 Jul 2024 in cs.OS

Abstract: Energy measurement of computer devices, which are widely used in the Internet of Things (IoT), is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. A cost-effective solution is to use low-end consumer-grade power meters. However, these low-end power meters cannot provide accurate instantaneous power measurements. In this paper, we propose an easy-to-use approach to derive an instantaneous software-based energy estimation model with only low-end power meters based on data-driven analysis through machine learning. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection method and physical measurement are explored. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92% accuracy can be achieved compared to the long-duration measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in real environment.

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References (58)
  1. M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for reduced cpu energy,” Mobile Computing, pp. 449–471, 1996.
  2. V. M. Weaver, M. Johnson, K. Kasichayanula, J. Ralph, P. Luszczek, D. Terpstra, and S. Moore, “Measuring energy and power with papi,” in 2012 41st international conference on parallel processing workshops.   IEEE, 2012, pp. 262–268.
  3. M. Walker, S. Bischoff, S. Diestelhorst, G. Merrett, and B. Al-Hashimi, “Hardware-validated cpu performance and energy modelling,” in 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).   IEEE, 2018, pp. 44–53.
  4. J. Choi, S. Park, and J. Ko, “Analyzing head-mounted ar device energy consumption on a frame rate perspective,” in 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2017, pp. 1–2.
  5. W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.
  6. J. Yan, Y. Huang, A. Gupta, A. Gupta, C. Liu, J. Li, and L. Cheng, “Energy-aware systems for real-time job scheduling in cloud data centers: A deep reinforcement learning approach,” Computers and Electrical Engineering, vol. 99, p. 107688, 2022.
  7. J. Chen, Y. He, Y. Zhang, P. Han, and C. Du, “Energy-aware scheduling for dependent tasks in heterogeneous multiprocessor systems,” Journal of Systems Architecture, vol. 129, p. 102598, 2022.
  8. J. Zhou, J. Sun, P. Cong, Z. Liu, X. Zhou, T. Wei, and S. Hu, “Security-critical energy-aware task scheduling for heterogeneous real-time mpsocs in iot,” IEEE Transactions on Services Computing, vol. 13, no. 4, pp. 745–758, 2019.
  9. A. Carroll and G. Heiser, “An analysis of power consumption in a smartphone,” in 2010 USENIX Annual Technical Conference (USENIX ATC 10), 2010.
  10. A. B. Jørgensen, T. S. Aunsborg, S. Bęczkowski, C. Uhrenfeldt, and S. Munk-Nielsen, “High-frequency resonant operation of an integrated medium-voltage sic mosfet power module,” IET Power Electronics, vol. 13, no. 3, pp. 475–482, 2020.
  11. K. Ammous, H. Morel, and A. Ammous, “Analysis of power switching losses accounting probe modeling,” IEEE transactions on Instrumentation and Measurement, vol. 59, no. 12, pp. 3218–3226, 2010.
  12. Intel, “Running average power limit energy reporting / cve-2020-8694 , cve-2020-8695 / intel-sa-00389,” https://www.intel.com/content/www/us/en/developer/articles/technical/software-security-guidance/advisory-guidance/running-average-power-limit-energy-reporting.html?wapkw=rapl, 2020.
  13. AMD, “Amd uprof,” https://www.amd.com/en/developer/uprof.html, 2023.
  14. E. O. Lange, J. M. Jose, S. Benedict, and M. Gerndt, “Automated energy modeling framework for microcontroller-based edge computing nodes,” in International Conference on Advanced Network Technologies and Intelligent Computing.   Springer, 2022, pp. 422–437.
  15. W. Liu, Y. Ni, X. Du, W. Li, L. Chen, Z. Zeng, and R. Xiao, “Measurement system for energy consumption of runtime software in embedded system,” in International Symposium on Intelligence Computation and Applications.   Springer, 2021, pp. 124–138.
  16. D. Gis, N. Büscher, and C. Haubelt, “Real-time power analysis of smart sensors using advanced debugging methods,” Micromachines, vol. 12, no. 11, p. 1276, 2021.
  17. A. Rahimifar, Y. Seifi Kavian, H. Kaabi, and M. Soroosh, “Predicting the energy consumption in software defined wireless sensor networks: a probabilistic markov model approach,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 9053–9066, 2021.
  18. R. P. Ltd, “Raspberry pi documentation,” 2012, last accessed 29 September 2023. [Online]. Available: https://www.raspberrypi.com/documentation/computers/raspberry-pi.html
  19. N. Corporation, “Jetson nano,” 2023, last accessed 29 September 2023. [Online]. Available: https://developer.nvidia.com/embedded/jetson-nano
  20. P. Liu, D. Da Silva, and L. Hu, “{{\{{DART}}\}}: A scalable and adaptive edge stream processing engine,” in 2021 USENIX Annual Technical Conference (USENIX ATC 21), 2021, pp. 239–252.
  21. M. A. Alsahli, A. Alsanad, M. M. Hassan, and A. Gumaei, “Privacy preservation of user identity in contact tracing for covid-19-like pandemics using edge computing,” IEEE Access, vol. 9, pp. 125 065–125 079, 2021.
  22. T. Zhou, H. Wang, X. Li, and M. Lin, “Profiling and understanding cpu power management in linux,” in 2023 IEEE Smart World Congress (SWC).   IEEE, 2023, pp. 1–8.
  23. L. HangZhou RuiDeng Technologies Co., “Instructions for usb tester with full colour display,” 2023, last accessed 29 September 2023. [Online]. Available: https://phuketshopper.com/software/UM25C/UM25C%20USB%20tester%20meter%20Instructions.pdf
  24. T. Zhou and M. Lin, “Deadline-aware deep-recurrent-q-network governor for smart energy saving,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 6, pp. 3886–3895, 2022.
  25. T. Zhou and M. Lin, “Cpu frequency scheduling of real-time applications on embedded devices with temporal encoding-based deep reinforcement learning,” Journal of Systems Architecture, vol. 142, p. 102955, 2023.
  26. F. Reghenzani, A. Bhuiyan, W. Fornaciari, and Z. Guo, “A multi-level dpm approach for real-time dag tasks in heterogeneous processors,” in 2021 IEEE Real-Time Systems Symposium (RTSS).   IEEE, 2021, pp. 14–26.
  27. B. Ranjbar, T. D. Nguyen, A. Ejlali, and A. Kumar, “Power-aware runtime scheduler for mixed-criticality systems on multicore platform,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 10, pp. 2009–2023, 2020.
  28. D. Zhu, R. Melhem, and B. R. Childers, “Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems,” IEEE transactions on parallel and distributed systems, vol. 14, no. 7, pp. 686–700, 2003.
  29. B. Xue, Y. Mao, S. B. Venkatakrishnan, and S. Kannan, “Goldfish: Peer selection using matrix completion in unstructured p2p network,” in 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).   IEEE, 2023, pp. 1–9.
  30. A. Bhuiyan, Z. Guo, A. Saifullah, N. Guan, and H. Xiong, “Energy-efficient real-time scheduling of dag tasks,” ACM Transactions on Embedded Computing Systems (TECS), vol. 17, no. 5, pp. 1–25, 2018.
  31. A. Bhuiyan, D. Liu, A. Khan, A. Saifullah, N. Guan, and Z. Guo, “Energy-efficient parallel real-time scheduling on clustered multi-core,” IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 9, pp. 2097–2111, 2020.
  32. Z. Guo, A. Bhuiyan, A. K. Di Liu, A. Saifullah, and N. Guan, “Energy-efficient real-time scheduling of dags on clustered multi-core platforms. in 2019 ieee real-time and embedded technology and applications symposium (rtas),” IEEE, 156ś168, 2019.
  33. J. Huang, H. Sun, F. Yang, S. Gao, and R. Li, “Energy optimization for deadline-constrained parallel applications on multi-ecu embedded systems,” Journal of Systems Architecture, vol. 132, p. 102739, 2022.
  34. Z. Li, S. Ren, and G. Quan, “Energy minimization for reliability-guaranteed real-time applications using dvfs and checkpointing techniques,” Journal of Systems Architecture, vol. 61, no. 2, pp. 71–81, 2015.
  35. A. Bakshi, Y. Mao, K. Srinivasan, and S. Parthasarathy, “Fast and efficient cross band channel prediction using machine learning,” in The 25th Annual International Conference on Mobile Computing and Networking, 2019, pp. 1–16.
  36. S. Ayvaz and K. Alpay, “Predictive maintenance system for production lines in manufacturing: A machine learning approach using iot data in real-time,” Expert Systems with Applications, vol. 173, p. 114598, 2021.
  37. S. K. Panda, M. Lin, and T. Zhou, “Energy-efficient computation offloading with dvfs using deep reinforcement learning for time-critical iot applications in edge computing,” IEEE Internet of Things Journal, vol. 10, no. 8, pp. 6611–6621, 2022.
  38. J. L. C. Hoffmann and A. A. Fröhlich, “Online machine learning for energy-aware multicore real-time embedded systems,” IEEE Transactions on Computers, vol. 71, no. 2, pp. 493–505, 2021.
  39. J.-G. Park, N. Dutt, and S.-S. Lim, “An interpretable machine learning model enhanced integrated cpu-gpu dvfs governor,” ACM Transactions on Embedded Computing Systems (TECS), vol. 20, no. 6, pp. 1–28, 2021.
  40. A. Das, G. V. Merrett, M. Tribastone, and B. M. Al-Hashimi, “Workload change point detection for runtime thermal management of embedded systems,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 8, pp. 1358–1371, 2015.
  41. Y. Wang and M. Pedram, “Model-free reinforcement learning and bayesian classification in system-level power management,” IEEE Transactions on Computers, vol. 65, no. 12, pp. 3713–3726, 2016.
  42. Y. Wang, W. Zhang, M. Hao, and Z. Wang, “Online power management for multi-cores: A reinforcement learning based approach,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 4, pp. 751–764, 2021.
  43. R. A. Shafik, S. Yang, A. Das, L. A. Maeda-Nunez, G. V. Merrett, and B. M. Al-Hashimi, “Learning transfer-based adaptive energy minimization in embedded systems,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 6, pp. 877–890, 2015.
  44. D. Ramegowda and M. Lin, “Can learning-based hybrid dvfs technique adapt to different linux embedded platforms?” in 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI), 2021, pp. 170–177.
  45. S. K. Rethinagiri, O. Palomar, R. Ben Atitallah, S. Niar, O. Unsal, and A. C. Kestelman, “System-level power estimation tool for embedded processor based platforms,” in Proceedings of the 6th Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, 2014, pp. 1–8.
  46. M. J. Walker, S. Diestelhorst, A. Hansson, A. K. Das, S. Yang, B. M. Al-Hashimi, and G. V. Merrett, “Accurate and stable run-time power modeling for mobile and embedded cpus,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 1, pp. 106–119, 2016.
  47. K. Nikov, K. Georgiou, Z. Chamski, K. Eder, and J. Nunez-Yanez, “Accurate energy modelling on the cortex-m0 processor for profiling and static analysis,” in 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS).   IEEE, 2022, pp. 1–4.
  48. D. Zoni, L. Cremona, and W. Fornaciari, “Powerprobe: Run-time power modeling through automatic rtl instrumentation,” in 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).   IEEE, 2018, pp. 743–748.
  49. D. Kim, J. Zhao, J. Bachrach, and K. Asanović, “Simmani: Runtime power modeling for arbitrary rtl with automatic signal selection,” in Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, pp. 1050–1062.
  50. Z. Xie, X. Xu, M. Walker, J. Knebel, K. Palaniswamy, N. Hebert, J. Hu, H. Yang, Y. Chen, and S. Das, “Apollo: An automated power modeling framework for runtime power introspection in high-volume commercial microprocessors,” in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, 2021, pp. 1–14.
  51. C. Zhan, M. Ghaderibaneh, P. Sahu, and H. Gupta, “Deepmtl: Deep learning based multiple transmitter localization,” in 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).   IEEE, 2021, pp. 41–50.
  52. C. Zhan, M. Ghaderibaneh, P. Sahu, and H. Gupta, “Deepmtl pro: Deep learning based multiple transmitter localization and power estimation,” Pervasive and Mobile Computing, vol. 82, p. 101582, 2022.
  53. K. Nikov and J. Nunez-Yanez, “Intra and inter-core power modelling for single-isa heterogeneous processors,” International Journal of Embedded Systems, vol. 12, no. 3, pp. 324–340, 2020.
  54. R. Lajara, J. Pelegrí-Sebastiá, and J. J. Perez Solano, “Power consumption analysis of operating systems for wireless sensor networks,” Sensors, vol. 10, no. 6, pp. 5809–5826, 2010.
  55. M. R. Guthaus, J. S. Ringenberg, D. Ernst, T. M. Austin, T. Mudge, and R. B. Brown, “Mibench: A free, commercially representative embedded benchmark suite,” in Proceedings of the fourth annual IEEE international workshop on workload characterization. WWC-4 (Cat. No. 01EX538).   IEEE, 2001, pp. 3–14.
  56. A. Kopytov, “Sysbench: a system performance benchmark,” http://sysbench. sourceforge. net/, 2004.
  57. A. Ash and M. Shwartz, “R2: a useful measure of model performance when predicting a dichotomous outcome,” Statistics in medicine, vol. 18, no. 4, pp. 375–384, 1999.
  58. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.

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