ABSTRACT
Technology scaling enables the design of low cost biosignal processing chips suited for emerging wireless body-area sensing applications. Energy consumption severely limits such applications and memories are becoming the energy bottleneck to achieve ultra-low-power operation. When aggressive voltage scaling is used, memory operation becomes unreliable due to the lack of sufficient Static Noise Margin. This paper introduces an approximate biosignal Compressed Sensing approach. We propose a digital architecture featuring a hybrid memory (6T-SRAM/SCMEM cells) designed to control perturbations on specific data structures. Combined with a statistically robust reconstruction algorithm, the system tolerates memory errors and achieves significant energy savings with low area overhead.
- http://www.who.int/mediacentre/factsheets/fs317/en.Google Scholar
- Ashouei, M. et al., "A voltage-scalable biomedical signal processor running ECG using 13pJ/cycle at 1MHz and 0.4 V", ISSCC, 2011.Google Scholar
- Rooseleer, B. and Wim D., "A 40 nm, 454MHz 114 fJ/bit area-efficient SRAM memory with integrated charge pump", ESSCIRC, 2013.Google ScholarCross Ref
- Sharma, V. et al. "8T SRAM with mimicked negative bit-lines and charge limited sequential sense amplifier for wireless sensor nodes", ESSCIRC, 2011.Google Scholar
- Verma, N., and A. P. Chandrakasan, "A 256 kb 65 nm 8T subthreshold SRAM employing sense-amplifier redundancy", Solid-State Circuits, IEEE Journal of 43.1 (2008): 141--149.Google ScholarCross Ref
- Andersson, O. et al., "Dual-VT 4kb sub-VT memories with < 1pW/bit leakage in 65 nm CMOS", ESSCIRC, 2013.Google Scholar
- Mamaghanian, H. et al., "Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes", IEEE Transactions Biomedical Engineering, vol. 58, no.9 pp. 2456--2466, 2011.Google ScholarCross Ref
- Dreslinkski, R. G., et al., "An energy efficient parallel architecture using near threshold operation", PACT, 2007. Google ScholarDigital Library
- Gemmeke, T. et al., "Resolving the Memory Bottleneck for Single Supply Near-Threshold Computing", DATE, 2014. Google ScholarDigital Library
- Dogan A.Y. et al., "Multi-core architecture design for ultra-low-power wearable health monitoring systems", DATE, 2012. Google ScholarDigital Library
- Calhoun, B. H. et al., "Analyzing static noise margin for sub-threshold SRAM in 65nm CMOS", ESSCIRC, 2005.Google Scholar
- Chang I.J. et al., "A Priority-Based 6T/8T Hybrid SRAM Architecture for Aggressive Voltage Scaling in Video Applications", IEEE transactions on circuits and systems for video technology, vol. 21, no. 2, Feb 2011. Google ScholarDigital Library
- Bortolotti D. et al., "Hybrid memory architecture for voltage scaling in ultra-low power multi-core biomedical processors", DATE, 2014. Google ScholarDigital Library
- Gupta, V. et al., "IMPACT: imprecise adders for low-power approximate computing", ISLPED, 2011. Google ScholarDigital Library
- Bortolotti D. et al., "VirtualSoC: a Full-System SimulationEnvironment for Massively Parallel Heterogeneous System-on-Chip", IPDPWS, 2013. Google ScholarDigital Library
- Beck A. and Teboulle M., "Fast iterative shrinkage-thresholding algorithm with application to wavelet-based image deblurring", ICASSP, 2009. Google ScholarDigital Library
- Candes E. et al., "Stable signal recovery from incomplete and inaccurate measurements", Communications on Pure and Applied Mathematics, 59:pages 1207--1223, 2006.Google ScholarCross Ref
- Chandar V., "A negative result concerning explicit matrices with the restricted isometry property", Tech. report, 2008.Google Scholar
- Nesterov, Y. "A method of solving a convex programming problem with convergence rate O(1/k2)", Soviet Mathematics Doklady. Vol. 27. No. 2. 1983.Google Scholar
- Goldberger A. L. et al., "Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals";, Circulation,101(23):pp. 215--220, 2000.Google ScholarCross Ref
- Mamaghanian H. et al., "Power-efficient joint compressed sensing of multi-lead ecg signals", ICASSP, 2014.Google ScholarCross Ref
- Kowalski M. et al., "Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients", Signal, Image and Video Processing, 3(3):pages 251--264, 2009.Google ScholarCross Ref
- Lin Z. et al., "The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices", arXiv preprint arXiv:1009.5055, 2010.Google Scholar
- Zhu, H. et al., "Sparsity-cognizant total least-squares for perturbed compressive sampling", Signal Processing, IEEE Transactions on 59, no 5 (2011): pp. 2002--2016. Google ScholarDigital Library
Index Terms
- Approximate compressed sensing: ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor
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