Abstract
Self-powered systems running on scavenged energy will be a key enabler for pervasive computing across the Internet of Things. The variability of input power in energy-harvesting systems limits the effectiveness of static optimizations aimed at maximizing the input-energy-to-computation ratio. We show that the resultant gap between available and exploitable energy is significant, and that energy storage optimizations alone do not significantly close the gap. We characterize these effects on a real, fabricated energy-harvesting system based on a nonvolatile processor. We introduce a unified energy-oriented approach to first optimize the number of backups, by more aggressively using the stored energy available when power failure occurs, and then optimize forward progress via improving the rate of input energy to computation via dynamic voltage and frequency scaling and self-learning techniques. We evaluate combining these schemes and show capture of up to 75.5% of all input energy toward processor computation, an average of 1.54 × increase over the best static “Forward Progress” baseline system. Notably, our energy-optimizing policy combinations simultaneously improve both the rate of forward progress and the rate of backup events (by up to 60.7% and 79.2% for RF power, respectively, and up to 231.2% and reduced to zero, respectively, for solar power). This contrasts with static frequency optimization approaches in which these two metrics are antagonistic.
- S. Baglio, C. Trigona, B. Ando, F. Maiorca, G. L’Episcopo, and A. Beninato. 2012. Energy harvesting from weak random vibrations: Bistable strategies and architectures for MEMS devices. In Proceedings of the 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS’12). 154--157. DOI:http://dx.doi.org/10.1109/MWSCAS.2012.6291980 Google Scholar
Cross Ref
- V. A. Boicea. 2014. Energy storage technologies: The past and the present. Proc. IEEE 102, 11 (Nov. 2014), 1777--1794. DOI:http://dx.doi.org/10.1109/JPROC.2014.2359545 Google Scholar
Cross Ref
- D. Brunelli, L. Benini, C. Moser, and L. Thiele. 2008. An efficient solar energy harvester for wireless sensor nodes. In Proceedings of the Design, Automation and Test in Europe, 2008 (DATE’08). 104--109.Google Scholar
Digital Library
- J. F. Christmann, E. Beigne, C. Condemine, and J. Willemin. 2010. An innovative and efficient energy harvesting platform architecture for autonomous microsystems. In Proceedings of the 2010 8th IEEE International NEWCAS Conference (NEWCAS’10). 173--176. DOI:http://dx.doi.org/10.1109/NEWCAS.2010.5603747 Google Scholar
Cross Ref
- A. Colin and B. Lucia. 2016. Chain: Tasks and channels for reliable intermittent programs. In Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications. ACM, 514--530. Google Scholar
Digital Library
- X. Cui, K. Ma, K. Liao, N. Liao, D. Wu, W. Wei, R. Li, and D. Yu. 2013. A dynamic-adjusting threshold-voltage scheme for FinFETs low power designs. In Proceedings of the 2013 IEEE International Symposium on Circuits and Systems (ISCAS’13). 129--132.Google Scholar
- Q. Deng, D. Meisner, A. Bhattacharjee, T. F. Wenisch, and R. Bianchini. 2012. CoScale: Coordinating CPU and memory system DVFS in server systems. In 45th Annual IEEE/ACM International Symposium on Microarchitecture. 143--154. Google Scholar
Digital Library
- S. George, K. Ma, A. Aziz, X. Li, A. Khan, S. Salahuddin, M.-F. Chang, S. Datta, J. Sampson, S. Gupta, and V. Narayanan. 2016. Nonvolatile memory design based on ferroelectric FETs. In Proceedings of the 53rd Annual Design Automation Conference. ACM, 118. Google Scholar
Digital Library
- M. A. Green, K. Emery, Y. Hishikawa, W. Warta, and E. D. Dunlop. 2014. Solar cell efficiency tables (version 43). Progress Photovoltaics Res. Appl. 22 (2014), 1--9. Google Scholar
Cross Ref
- Intel. Intel®turbo boost technology 2.0. http://www.intel.com/content/www/us/en/architecture-and-technology/turbo-boost/turbo-boost-technology.html.Google Scholar
- M. Kaisheng, L. M. Julie, L. Xueqing, H. Zhixuan, and S. Jack. 2017. Evaluating tradeoffs in granularity and overheads in supporting nonvolatile execution semantics. In The 18th International Symposium on Quality Electronic Design (ISQED'17). Santa Clara.Google Scholar
- A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava. 2007. Power management in energy harvesting sensor networks. ACM Trans. Embed. Comput. Syst. 6, 4, Article 32 (Sept. 2007). Google Scholar
Digital Library
- A. Kansal and M. B. Srivastava. 2003. An environmental energy harvesting framework for sensor networks. In Proceedings of the 2003 International Symposium on Low Power Electronics and Design, 2003 (ISLPED’03). 481--486. Google Scholar
Digital Library
- S. Kim, R. Vyas, J. Bito, K. Niotaki, A. Collado, A. Georgiadis, and M. M. Tentzeris. 2014. Ambient RF energy-harvesting technologies for self-sustainable standalone wireless sensor platforms. Proc. IEEE 102, 11 (2014), 1649--1666. Google Scholar
Cross Ref
- H. Kimura, Z. Zhong, Y. Mizuochi, N. Kinouchi, Y. Ichida, and Y. Fujimori. 2013. Highly reliable non-volatile logic circuit technology and its application. In Proceedings of the 2013 IEEE 43rd International Symposium on Multiple-Valued Logic (ISMVL’13). 212--218. Google Scholar
Digital Library
- X. Li, U. D. Heo, K. Ma, H. Liu, V. Narayanan, and S. Datta. 2014a. RF-powered systems using steep-slope devices. In Proceedings of the IEEE International New Circuits and Systems Conference.Google Scholar
- X. Li, H. Liu, U. D. Heo, K. Ma, S. Datta, and V. Narayanan. 2014b. RF-powered systems using steep-slope devices. In Proceedings of the New Circuits and Systems Conference (NEWCAS’14). 73--76. Google Scholar
Cross Ref
- X. Li, K. Ma, S. George, J. Sampson, and V. Narayanan. 2016. Enabling internet-of-things: Opportunities brought by emerging devices, circuits, and architectures. In 2016 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC). 1--6. Google Scholar
Cross Ref
- X. Lin, Y. Wang, S. Yue, N. Chang, and M. Pedram. 2013. A framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting. In Proceedings of the 2013 International Symposium on Low Power Electronics and Design (ISLPED’13). IEEE Press, 70--75. Google Scholar
Cross Ref
- Y. Liu, Z. Li, H. Li, Y. Wang, X. Li, K. Ma, S. Li, M.-F. Chang, S. John, Y. Xie, J. Shu, and H. Yang. 2015. Ambient energy harvesting nonvolatile processors: From circuit to system. In Proceedings of the 52nd Annual Design Automation Conference. ACM, 150. Google Scholar
Digital Library
- K. Ma, X. Li, S. Li, Y. Liu, J. J. Sampson, Y. Xie, and V. Narayanan. 2015a. Nonvolatile processor architecture exploration for energy-harvesting applications. IEEE Micro 35, 5 (2015), 32--40. Google Scholar
Digital Library
- K. Ma, X. Li, Y. Liu, J. Sampson, Y. Xie, and V. Narayanan. 2015b. Dynamic machine learning based matching of nonvolatile processor microarchitecture to harvested energy profile. In Proceedings of the 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD’15). IEEE, 670--675. Google Scholar
Cross Ref
- K. Ma, X. Li, J. Sampson, Y. Liu, Y. Xie, and V. Narayanan. 2015c. Nonvolatile processor optimization for ambient energy harvesting scenarios. In Proceedings of the 15th Non-Volatile Memory Technology Symposium.Google Scholar
- K. Ma, X. Li, S. R. Srinivasa, Y. Liu, J. Sampson, Y. Xie, and V. Narayanan. 2017. Spendthrift: Machine learning based resource and frequency scaling for ambient energy harvesting nonvolatile processors. In Proceedings of the 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 678--683. Google Scholar
Cross Ref
- K. Ma, X. Li, K. Swaminathan, Y. Zheng, S. Li, Y. Liu, J. Sampson, Y. Xie, and V. Narayanan. 2016. Nonvolatile processor architectures: Efficient, reliable progress with unstable power. IEEE Micro 36, 3 (2016), 72--83. Google Scholar
Cross Ref
- K. Ma, Y. Zheng, S. Li, K. Swaminathan, X. Li, Y. Liu, J. Sampson, Y. Xie, and V. Narayanan. 2015. Architecture exploration for ambient energy harvesting nonvolatile processors. In Proceedings of the 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA’15). 526--537. Google Scholar
Cross Ref
- P. P. Mercier, S. Bandyopadhyay, A. C. Lysaght, K. M. Stankovic, and A. P. Chandrakasan. 2013. A 78 pW 1 b/s 2.4 GHz radio transmitter for near-zero-power sensing applications. In 2013 Proceedings of the ESSCIRC. 133--136. DOI:http://dx.doi.org/10.1109/ESSCIRC.2013.6649090 Google Scholar
Cross Ref
- R. Miftakhutdinov, E. Ebrahimi, and Y. N. Patt. 2012. Predicting performance impact of DVFS for realistic memory systems. In Proceedings of the 45th Annual IEEE/ACM International Symposium on Microarchitecture. 155--165. Google Scholar
Digital Library
- D. Porcarelli, D. Brunelli, M. Magno, and L. Benini. 2012. A multi-harvester architecture with hybrid storage devices and smart capabilities for low power systems. In Proceedings of the 2012 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM’12). 946--951. DOI:http://dx.doi.org/10.1109/SPEEDAM.2012.6264533 Google Scholar
Cross Ref
- N. B. Rizv and A. Y. Zomaya. 2013. A Primarily Survey on Energy Efficiency in Cloud and Distributed Computing Systems. arXiv preprint (Oct 2013).Google Scholar
- S. Roundy, D. Steingart, L. Frechette, P. Wright, and J. Rabaey. 2004. Power sources for wireless sensor networks. In Wireless Sensor Networks. Springer, 1--17. Google Scholar
Cross Ref
- X. Sheng, C. Wang, Y. Liu, H. G. Lee, N. Chang, and H. Yang. 2014. A high-efficiency dual-channel photovoltaic power system for nonvolatile sensor nodes. In Proceedings of the 2014 IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA’14). 1--2. Google Scholar
Cross Ref
- K. Shoji, Y. Akiyama, M. Suzuki, N. Nakamura, H. Ohno, and K. Morishima. 2014. Diffusion refueling biofuel cell mountable on insect. In Proceedings of the 2014 IEEE 27th International Conference on Micro Electro Mechanical Systems (MEMS’14). IEEE, 163--166. Google Scholar
Cross Ref
- H. J. Siegel, B. Khemka, R. Friese, S. Pasricha, A. A. Maciejewski, G. A. Koenig, S. Powers, M. Hilton, R. Rambharos, G. Okonski, and S. Poole. 2014. Energy-aware resource management for computing systems. In Proceedings of the 2014 Seventh International Conference on Contemporary Computing (IC3’14). 7--12. Google Scholar
Cross Ref
- J. Van Der Woude and M. Hicks. 2016. Intermittent computation without hardware support or programmer intervention. In Proceedings of 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16). 17.Google Scholar
- C. Vanhecke, L. Assouere, A. Wang, P. Durand-Estebe, F. Caignet, J.-M. Dilhac, and M. Bafleur. 2015. Multisource and battery-free energy harvesting architecture for aeronautics applications. IEEE Transactions on Power Electronics 30, 6 (2015), 3215--3227. Google Scholar
Cross Ref
- C. Wang, N. Chang, Y. Kim, S. Park, Y. Liu, H. G. Lee, R. Luo, and H. Yang. 2014. Storage-less and converter-less maximum power point tracking of photovoltaic cells for a nonvolatile microprocessor. In Proceedings of the 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC’14). 379--384. Google Scholar
Cross Ref
- Y. Wang, Y. Liu, S. Li, D. Zhang, B. Zhao, M.-F. Chiang, Y. Yan, B. Sai, and H. Yang. 2012. A 3us wake-up time nonvolatile processor based on ferroelectric flip-flops. In ESSCIRC (ESSCIRC’12). 149--152.Google Scholar
- Q. Wu, V. J. Reddi, Y. Wu, J. Lee, D. Connors, D. Brooks, M. Martonosi, and D. W. Clark. 2005. A dynamic compilation framework for controlling microprocessor energy and performance. In Proceedings of the 38th Annual IEEE/ACM International Symposium on Microarchitecture. 271--282.Google Scholar
- Y. Xiang and S. Pasricha. 2014a. Fault-aware application scheduling in low-power embedded systems with energy harvesting. In Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis (CODES’14). Article 32, 10 pages. Google Scholar
Digital Library
- Y. Xiang and S. Pasricha. 2014b. A hybrid framework for application allocation and scheduling in multicore systems with energy harvesting. In Proceedings of the 24th Edition of the Great Lakes Symposium on VLSI. ACM, 163--168. Google Scholar
Digital Library
- R. Yaqub, H. Ahmad, N. A. Boakye-Boateng, and Y. Wang. 2012. System architecture for ride portfolio reporting employing energy harvesting scheme. In Proceedings of the 2012 International Conference on Connected Vehicles and Expo (ICCVE’12). 241--245. DOI:http://dx.doi.org/10.1109/ICCVE.2012.54 Google Scholar
Digital Library
Index Terms
Dynamic Power and Energy Management for Energy Harvesting Nonvolatile Processor Systems
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