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Determining Application-Specific Peak Power and Energy Requirements for Ultra-Low-Power Processors

Published:26 December 2017Publication History
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Abstract

Many emerging applications such as the Internet of Things, wearables, implantables, and sensor networks are constrained by power and energy. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to overprovisioning that increases system size and weight. In this article, we present an automated technique that performs hardware–software coanalysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application’s input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.

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References

  1. Allaboutbatteries.com. 2015. Battery Energy. Retrieved November 4, 2017 from http://www.allaboutbatteries.com/Battery-Energy.html. (2015).Google ScholarGoogle Scholar
  2. ARM Mbed. Welcome to Mbed. Retrieved November 6, 2017 from https://www.mbed.com/en/.Google ScholarGoogle Scholar
  3. John Greenough. 2015. The Internet of Everything: 2015 {Slide Deck}. Business Insider. http://www.businessinsider.com/internet-of-everything-2015-bi-2014-12Google ScholarGoogle Scholar
  4. Jacob Borgeson. 2012. Ultra-low-power pioneers: TI slashes total MCU power by 50 percent with new “Wolverine” MCU platform. Texas Instruments White Paper. Retrieved November 6, 2017 from http://www.ti.com/lit/wp/slay019a/slay019a.pdf.Google ScholarGoogle Scholar
  5. Randal E. Bryant. 1991. Symbolic simulation – Techniques and applications. In Proceedings of the 27th ACM/IEEE Design Automation Conference. ACM, 517--521. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Isidor Buchmann. 2016. The secrets of battery runtime. Battery University. Retrieved August 15, 2016 from http://batteryuniversity.com/learn/archive/the_secrets_of_battery_runtime.Google ScholarGoogle Scholar
  7. Cristian Cadar and Koushik Sen. 2013. Symbolic execution for software testing: Three decades later. Communications of the ACM 56, 2, 82--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cadence. 2014. Encounter Digital Implementation User Guide. Version: 14.11. http://www.cadence.com/.Google ScholarGoogle Scholar
  9. B. H. Calhoun, S. Khanna, Yanqing Zhang, J. Ryan, and B. Otis. 2010. System design principles combining sub-threshold circuit and architectures with energy scavenging mechanisms. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS’10). 269--272.Google ScholarGoogle Scholar
  10. Hari Cherupalli, Rakesh Kumar, and John Sartori. 2016. Exploiting dynamic timing slack for energy efficiency in ultra-low-power embedded systems. In 43th Annual International Symposium on Computer Architecture (ISCA’16). IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Cloud Compiling. 2013. Welcome to Cloud Compiling. Retrieved November 6, 2017 from http://www.cloudcompiling.com/.Google ScholarGoogle Scholar
  12. Adam Dunkels, Joakim Eriksson, Niclas Finne, Fredrik Osterlind, Nicolas Tsiftes, Julien Abeillé, and Mathilde Durvy. 2012. Low-power IPv6 for the Internet of Things. In 9th International Conference on Networked Sensing Systems (INSS’12). IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  13. EEMBC. 2017. Embedded Microprocessor Benchmark Consortium. Retrieved November 6, 2017 from http://www.eembc.org.Google ScholarGoogle Scholar
  14. Dave Evans. 2011. The Internet of Things: How the next evolution of the Internet is changing everything. Retrieved August 15, 2016 from https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf.Google ScholarGoogle Scholar
  15. Tao Feng, L. C. Wang, Kwang-Ting Cheng, M. Pandey, and M. S. Abadir. 2003. Enhanced symbolic simulation for efficient verification of embedded array systems. In Proceedings of the 2003 ASP-DAC Asia and South Pacific Design Automation Conference. 302--307. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Kjartan Furset and Peter Hoffman. 2011. High pulse drain impact on CR2032 coin cell battery capacity. Nordic Semiconductor and Energizer. Retrieved August 15, 2016 from https://m.eet.com/media/1121454/c0924post.pdf.Google ScholarGoogle Scholar
  17. O. Girard. 2013. OpenMSP430 project. Opencores.org. Retrieved March 1, 2014 from https://opencores.org/project,openmsp430.Google ScholarGoogle Scholar
  18. M. R. Guthaus, J. S. Ringenberg, D. Ernst, T. M. Austin, T. Mudge, and R. B. Brown. 2001. MiBench: A free, commercially representative embedded benchmark suite. In Proceedings of the 2001 IEEE International Workshop Workload Characterization (WWC’01). 3--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. K. Hamaguchi. 2001. Symbolic simulation heuristics for high-level design descriptions with uninterpreted functions. In Proceedings of the 6th IEEE International High-Level Design Validation and Test Workshop. 25--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Michael S. Hsiao. 1999. Peak power estimation using genetic spot optimization for large VLSI circuits. In Proceedings of the Conference on Design, Automation and Test in Europe. ACM, 38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Michael S. Hsiao, Elizabeth M. Rudnick, and Janak H. Patel. 1997. K2: An estimator for peak sustainable power of VLSI circuits. In Proceedings of the 1997 International Symposium on Low Power Electronics and Design. IEEE, 178--183. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. ITRS. 2015. International Technology Roadmap for Semiconductors 2.0 2015 Edition Executive Report. 2015. Retrieved November 6, 2017 from http://www.semiconductors.org/main/2015_international_technology_roadmap_for_semiconductors_itrs/.Google ScholarGoogle Scholar
  23. P. Jain and G. Gopalakrishnan. 1994. Efficient symbolic simulation-based verification using the parametric form of Boolean expressions. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 13, 8, 1005--1015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Ramkumar Jayaseelan, Tulika Mitra, and Xianfeng Li. 2006. Estimating the worst-case energy consumption of embedded software. In 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’06). IEEE, 81--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Youngtaek Kim, Lizy Kurian John, Sanjay Pant, Srilatha Manne, Michael Schulte, W. Lloyd Bircher, and Madhu S. Sibi Govindan. 2012. AUDIT: Stress testing the automatic way. In Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’12). IEEE Computer Society, Washington, DC, USA, 212--223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. A. Kolbi, J. Kukula, and R. Damiano. 2001. Symbolic RTL simulation. In Proceedings of the 2001 Design Automation Conference. 47--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Vasileios Kontorinis, Amirali Shayan, Dean M. Tullsen, and Rakesh Kumar. 2009. Reducing peak power with a table-driven adaptive processor core. In Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’09). ACM, New York, NY, USA, 189--200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. L. Liu and S. Vasudevan. 2011. Efficient validation input generation in RTL by hybridized source code analysis. In Design, Automation Test in Europe Conference Exhibition (DATE’11). 1--6.Google ScholarGoogle Scholar
  29. Michele Magno, Luca Benini, Christian Spagnol, and E. Popovici. 2013. Wearable low power dry surface wireless sensor node for healthcare monitoring application. In IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob’13). IEEE, 189--195.Google ScholarGoogle Scholar
  30. Jeremy Morse, Steve Kerrison, and Kerstin Eder. 2016. On the infeasibility of analysing worst-case dynamic energy. arXiv Preprint arXiv:1603.02580.Google ScholarGoogle Scholar
  31. K. Najeeb, V. Vardhan, R. Konda, S. Kumar, S. Hari, V. Kamakoti, and V. M. Vedula. 2007. Power virus generation using behavioral models of circuits. In 25th IEEE VLSI Test Symposium. 35--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. National Instruments. 2016. Compile Faster with the LabVIEW FPGA Compile Cloud Service. Retrieved November 6, 2017 from http://www.ni.com/white-paper/52328/en/.Google ScholarGoogle Scholar
  33. Intel Corporation. 2000. Intel Pentium 4 Processor in the 423-pin Package Thermal Design Guidelines. Retrieval August 15, 2016 from http://download.intel.com/support/processors/pentium4/sb/24920301.pdf.Google ScholarGoogle Scholar
  34. IC Insights. 2005. Microcontroller Sales Regain Momentum After Slump. Retrieval date August 15, 2016 from http://www.icinsights.com/news/bulletins/Microcontroller-Sales-Regain-Momentum-After-Slump/.Google ScholarGoogle Scholar
  35. J. A. Paradiso and T. Starner. 2005. Energy scavenging for mobile and wireless electronics. IEEE Pervasive Computing 4, 1, 18--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Chulsung Park, Pai H. Chou, Ying Bai, Robert Matthews, and Andrew Hibbs. 2006. An ultra-wearable, wireless, low power ECG monitoring system. In IEEE Biomedical Circuits and Systems Conference (BioCAS’06). IEEE, 241--244.Google ScholarGoogle ScholarCross RefCross Ref
  37. Gil Press. 2014. Internet of Things by the numbers: Market estimates and forecasts. Forbes. Retrieval Aug 15, 2016 from https://www.forbes.com/sites/gilpress/2014/08/22/internet-of-things-by-the-numbers-market-estimates-and-forecasts/.Google ScholarGoogle Scholar
  38. Sriram Sambamurthy, Sankar Gurumurthy, Ramtilak Vemu, and Jacob A. Abraham. 2009. Functionally valid gate-level peak power estimation for processors. In Quality of Electronic Design (ISQED’09). IEEE, 753--758. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. J. Sartori and R. Kumar. 2009. Distributed peak power management for many-core architectures. In Design, Automation Test in Europe Conference Exhibition (DATE’09).1556--1559. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Kiran Seth, Aravindh Anantaraman, Frank Mueller, and Eric Rotenberg. 2006. Fast: Frequency-aware static timing analysis. ACM Transactions on Embedded Computing Systems 5, 1, 200--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Synopsys. 2015. Design Compiler User Guide. Version: K-2015.06. http://www.synopsys.com/.Google ScholarGoogle Scholar
  42. Synopsys. 2015. PrimeTime User Guide. Version: K-2015.06-SP2. http://www.synopsys.com/.Google ScholarGoogle Scholar
  43. Russell Tessier, David Jasinski, Atul Maheshwari, Aiyappan Natarajan, Weifeng Xu, and Wayne Burleson. 2005. An energy-aware active smart card. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 13, 10, 1190--1199. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Texas Instruments. 2013. eZ430-RF2500-SEH solar energy harvesting development tool user’s guide. Retrieved November 6, 2017 from http://www.ti.com/lit/ug/slau273d/slau273d.pdf, (2013).Google ScholarGoogle Scholar
  45. Peter Wägemann, Tobias Distler, Timo Hönig, Heiko Janker, Rüdiger Kapitza, and Wolfgang Schröder-Preikschat. 2015. Worst-case energy consumption analysis for energy-constrained embedded systems. In 27th Euromicro Conference on Real-Time Systems. IEEE, 105--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Chuan-Yu Wang and Kaushik Roy. 1998. Maximum power estimation for CMOS circuits using deterministic and statistical approaches. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 6, 1, 134--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Wikipedia. 2016. List of wireless sensor nodes. Retrieved November 6, 2017 from https://en.wikipedia.org/wiki/List_of_wireless_sensor_nodes.Google ScholarGoogle Scholar
  48. Ross Yu and Thomas Watteyne. 2013. Reliable, low power wireless sensor networks for the Internet of Things: Making wireless sensors as accessible as web servers. Linear Technology Retrieved November 6, 2017 from http://cds.linear.com/docs/en/white-paper/wp003.pdf.Google ScholarGoogle Scholar
  49. Bo Zhai, Sanjay Pant, Leyla Nazhandali, Scott Hanson, Javin Olson, Anna Reeves, Michael Minuth, Ryan Helfand, Todd Austin, Dennis Sylvester, and others. 2009. Energy-efficient subthreshold processor design. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 17, 8, 1127--1137. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Y. Zhang, Z. Chen, and J. Wang. 2012. Speculative symbolic execution. In IEEE 23rd International Symposium on Software Reliability Engineering (ISSRE’12). 101--110. Google ScholarGoogle ScholarDigital LibraryDigital Library

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