Abstract
The energy efficiency of digital architectures is tightly linked to the voltage level (Vdd) at which they operate. Aggressive voltage scaling is therefore mandatory when ultra-low power processing is required. Nonetheless, the lowest admissible Vdd is often bounded by reliability concerns, especially since static and dynamic non-idealities are exacerbated in the near-threshold region, imposing costly guard-bands to guarantee correctness under worst-case conditions. A striking alternative, explored in this paper, waives the requirement for unconditional correctness, undergoing more relaxed constraints. First, after a run-time failure, processing correctly resumes at a later point in time. Second, failures induce a limited Quality-of-Service (QoS) degradation. We focus our investigation on the practical scenario of embedded bio-signal analysis, a domain in which energy efficiency is key, while applications are inherently error-tolerant to a certain degree. Targeting a domain-specific multi-core platform, we present a study of the impact of inexactness on application-visible errors. Then, we introduce a novel methodology to manage them, which requires minimal hardware resources and a negligible energy overhead. Experimental evidence show that, by tolerating 900 errors/hour, the resulting inexact platform can achieve an efficiency increase of up to 24%, with a QoS degradation of less than 3%.
- H. Alemdar et al. 2010. Wireless sensor networks for healthcare: A survey. Computer Networks 54, 15 (October 2010), 2688--2710. Google Scholar
Digital Library
- M. Ashouei et al. 2011. A voltage-scalable biomedical signal processor running ECG using 13pJ/cycle at 1MHz and 0.4 V. In Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2011 IEEE International. IEEE, 332--334.Google Scholar
- L. Atzori et al. 2010. The internet of things: A survey. Computer Networks 54, 15 (October 2010), 2787--2805. Google Scholar
Digital Library
- S. Borkar et al. 2003. Parameter variations and impact on circuits and microarchitecture. In Proceedings of the 40th annual Design Automation Conference. ACM, 338--342. Google Scholar
Digital Library
- D. Bortolotti et al. 2014. Approximate compressed sensing: ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor. In Proceedings of the 2014 International Symposium on Low Power Electronics and Design. ACM, 45--50. Google Scholar
Digital Library
- R. Braojos et al. 2014. Hardware/software approach for code synchronization in low-power multi-core sensor nodes. In Design, Automation and Test in Europe Conference and Exhibition (DATE), 2014. IEEE, 1--6. Google Scholar
Digital Library
- R. Braojos et al. 2014. Ultra-low power design of wearable cardiac monitoring systems. In Proceedings of the 51st Annual Design Automation Conference. ACM, 1--6. Google Scholar
Digital Library
- R. Braojos et al. 2016. A synchronization-based hybrid-memory multi-core architecture for energy-efficient biomedical signal processing. IEEE Trans. Comput. (September 2016). Google Scholar
Digital Library
- J. Constantin et al. 2012. TamaRISC-CS: An ultra-low power application-specific processor for compressed sensing. In VLSI and System-on-Chip (VLSI-SoC), 2012 IEEE/IFIP 20th International Conference on. IEEE, 159--164.Google Scholar
- R. H. Dennard et al. 1974. Design of ion-implanted MOSFET’s with very small physical dimensions. IEEE Journal of Solid-State Circuits 9, 5 (January 1974), 256--268.Google Scholar
Cross Ref
- A. Dogan et al. 2013. Synchronizing code execution on ultra-low power embedded multi-channel signal analysis platforms. In Design, Automation 8 Test in Europe Conference 8 Exhibition (DATE), 2013. IEEE, 396--399. Google Scholar
Digital Library
- R. G. Dreslinski et al. 2010. Near-threshold computing: Reclaiming moore’s law through energy efficient integrated circuits. Proc. IEEE 98, 2 (January 2010), 253--266.Google Scholar
Cross Ref
- H. Esmaeilzadeh et al. 2012. Architecture support for disciplined approximate programming. In ACM SIGPLAN Notices, Vol. 47. ACM, 301--312. Google Scholar
Digital Library
- EU-MEP. 2015. Cardiovascular diseases facts and figures. www.mepheartgroup.eu/index.php/facts-a-figures. (2015).Google Scholar
- P. Ghanta et al. 2005. Stochastic power grid analysis considering process variations. In Proceedings of the conference on Design, Automation and Test in Europe-Volume 2. IEEE Computer Society, 964--969. Google Scholar
Digital Library
- C. Gomez et al. 2010. Wireless home automation networks: A survey of architectures and technologies. IEEE Communications Magazine 48, 6 (May 2010), 92--101. Google Scholar
Digital Library
- P. Gupta et al. 2013. Underdesigned and Opportunistic Computing in Presence of Hardware Variability. Trans. Comp.-Aided Des. Integ. Cir. Sys. 32, 1 (Jan. 2013), 8--23. Google Scholar
Digital Library
- Y. Hao et al. 2008. Wireless body sensor networks for health-monitoring applications. Physiological Measurement 29, 11 (October 2008), R27.Google Scholar
Cross Ref
- Y. He et al. 2010. Xetal-pro: an ultra-low energy and high throughput SIMD processor. In Proceedings of the 47th Design Automation Conference. ACM, 543--548. Google Scholar
Digital Library
- K. J. Heilman et al. 2007. Accuracy of the LifeShirt®(Vivometrics) in the detection of cardiac rhythms. Biological Psychology 75, 3 (July 2007), 300--305.Google Scholar
Cross Ref
- Texas Instruments. 2013. 2.4-GHz Bluetooth® low energy System-on-Chip. www.ti.com/lit/ds/symlink/cc2540.pdf. (June 2013).Google Scholar
- ITRS. 2016. International Technology Roadmap for Semiconductors. www.itrs2.net/. (2016).Google Scholar
- U. R. Karpuzcu et al. 2012. VARIUS-NTV: A microarchitectural model to capture the increased sensitivity of manycores to process variations at near-threshold voltages. In Dependable Systems and Networks (DSN), 2012 42nd Annual IEEE/IFIP International Conference on. IEEE, 1--11. Google Scholar
Digital Library
- S. Khare et al. 2013. Prospects of near-threshold voltage design for green computing. In VLSI Design and 2013 12th International Conference on Embedded Systems (VLSID), 2013 26th International Conference on. IEEE, 120--124. Google Scholar
Digital Library
- P. K. Krause et al. 2011. Adaptive voltage over-scaling for resilient applications. In 2011 Design, Automation Test in Europe. 1--6.Google Scholar
- J. Kwong et al. 2011. An energy-efficient biomedical signal processing platform. IEEE Journal of Solid-State Circuits 46, 7 (June 2011), 1742--1753.Google Scholar
Cross Ref
- X. Li et al. 2007. Application-level correctness and its impact on fault tolerance. In Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture (HPCA’07). IEEE Computer Society, Washington, DC, USA, 181--192. Google Scholar
Digital Library
- J. W. S. Liu et al. 1994. Imprecise computations. Proc. IEEE 82, 1 (Jan 1994), 83--94.Google Scholar
Cross Ref
- S. Lobodzinski. 2013. ECG patch monitors for assessment of cardiac rhythm abnormalities. Progress in Cardiovascular Diseases 56, 2 (September 2013), 224--229.Google Scholar
Cross Ref
- A. Mainwaring et al. 2002. Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications. ACM, 88--97. Google Scholar
Digital Library
- W. K. Mak et al. 2007. Voltage island generation under performance requirement for SoC designs. In Proceedings of the 2007 Asia and South Pacific Design Automation Conference. IEEE Computer Society, 798--803. Google Scholar
Digital Library
- H. Mamaghanian et al. 2011. Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering 58, 9 (May 2011), 2456--2466.Google Scholar
Cross Ref
- S. Mittal. 2016. A survey of architectural techniques for near-threshold computing. ACM Journal on Emerging Technologies in Computing Systems (JETC) 12, 4 (July 2016), 46. Google Scholar
Digital Library
- S. Mittal. 2016. A survey of techniques for approximate computing. ACM Computing Surveys (CSUR) 48, 4 (May 2016), 62. Google Scholar
Digital Library
- Y. Morita et al. 2007. An area-conscious low-voltage-oriented 8T-SRAM design under DVS environment. In VLSI Circuits, 2007 IEEE Symposium on. IEEE, 256--257.Google Scholar
Cross Ref
- S. S. Mukherjee et al. 2005. The Soft Error Problem: An Architectural Perspective. In Proceedings of the 11th International Symposium on High-Performance Computer Architecture (HPCA’05). IEEE Computer Society, Washington, DC, USA, 243--247. Google Scholar
Digital Library
- S. Mukhopadhyay et al. 2005. Modeling of failure probability and statistical design of SRAM array for yield enhancement in nanoscaled CMOS. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems 24, 12 (November 2005), 1859--1880. Google Scholar
Digital Library
- A. Pantelopoulos et al. 2010. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 40, 1 (January 2010), 1--12. Google Scholar
Digital Library
- PhysioBank. 2012. MIT-BIH Normal Sinus Rhythm Database. https://www.physionet.org/physiobank/database/nsrdb/. (February 2012).Google Scholar
- J. Schlachter et al. 2017. Design and Applications of Approximate Circuits by Gate-Level Pruning. IEEE Transactions on Very Large Scale Integration (VLSI) Systems (February 2017). Google Scholar
Digital Library
- M. Seok et al. 2008. The Phoenix Processor: A 30pW platform for sensor applications. In VLSI Circuits, 2008 IEEE Symposium on. IEEE, 188--189.Google Scholar
Cross Ref
- S. R. Sridhara et al. 2011. Microwatt embedded processor platform for medical system-on-chip applications. IEEE Journal of Solid-State Circuits 46, 4 (February 2011), 721--730.Google Scholar
Cross Ref
- Y. Sun et al. 2002. ECG signal conditioning by morphological filtering. Computers in Biology and Medicine 32, 6 (November 2002), 465--479.Google Scholar
Cross Ref
- Synopsys. 2017. ASIP Designer. www.synopsys.com/dw/ipdir.php?ds&equation;asip-designer. (2017).Google Scholar
- G. V. Varatkar et al. 2008. Error-resilient Motion Estimation Architecture. IEEE Trans. Very Large Scale Integr. Syst. 16, 10 (Oct. 2008), 1399--1412. Google Scholar
Digital Library
- WHO. 2017. The top 10 causes of death (Fact sheet no 310). www.who.int/mediacentre/factsheets/fs310/en/. (January 2017).Google Scholar
- F. Zhang et al. 2012. Design of ultra-low power biopotential amplifiers for biosignal acquisition applications. IEEE Transactions on Biomedical Circuits and Systems 6, 4 (January 2012), 344--355.Google Scholar
- M. Zhang et al. 2006. Soft-error-rate-analysis (SERA) methodology. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 25, 10 (August 2006), 2140--2155. Google Scholar
Digital Library
- X. Zhang et al. 2013. Characterizing and evaluating voltage noise in multi-core near-threshold processors. In Proceedings of the 2013 International Symposium on Low Power Electronics and Design. IEEE Press, 82--87. Google Scholar
Digital Library
Index Terms
An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery
Recommendations
Ultra low power digital signal processing
VLSID '96: Proceedings of the 9th International Conference on VLSI Design: VLSI in Mobile CommunicationThe explosive growth of portable wireless devices has elevated power consumption to be one of the most critical design parameters. This paper presents several techniques to implement DSP functions with the lowest possible power consumption. Since power ...
Ultra-Low Power Read-Decoupled SRAMs with Ultra-Low Write-Bitline Voltage Swing
We propose an ultra-low power memory design method based on the ultra-low ( $$\sim $$ ~ 0.2 V) write-bitline voltage swing to reduce the write power dissipation for read-decoupled SRAM (RD-SRAM) cells. By keeping the write bitlines at ground level (0 V) during ...
Complementary nano-electromechanical switches for ultra-low power embedded processors
GLSVLSI '09: Proceedings of the 19th ACM Great Lakes symposium on VLSIIdle power consumption is becoming an important parameter in portable embedded system design where excessive quiescent power dissipation can lead to excessive heat generation and reliability issues in nanometer-scale CMOS. To address some of these ...






Comments