TY - GEN
T1 - Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits
AU - Ma, Yufei
AU - Kim, Minkyu
AU - Cao, Yu
AU - Seo, Jae-sun
AU - Vrudhula, Sarma
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/14
Y1 - 2015/12/14
N2 - Compressive sensing (CS) allows acquiring sparse signals at sub-Nyquist rate, offering an energy-efficient solution to data acquisition. This is especially important to reduce communication data for mobile medical applications. However, reconstructing the signal from CS is usually left off-line due to the complex computations. In this paper, we integrate two key technologies to enable on-line energy-efficient CS signal reconstruction. These are (1) the use of Bayesian CS Belief Propagation (CS-BP) as the algorithm basis and (2) the novel design of stochastic computing (SC) circuits to efficiently map CS-BP algorithm. The overall signal reconstruction system is implemented with digital SC circuits in 65nm CMOS and recovers compressively sensed electrocardiography (ECG) and electromyography (EMG) signals with 11X to 8X data compression factor. Compared to a conventional binary design, post-layout simulation results show that the proposed stochastic design performs reconstruction with 5X energy-delay product improvement and 2X area reduction.
AB - Compressive sensing (CS) allows acquiring sparse signals at sub-Nyquist rate, offering an energy-efficient solution to data acquisition. This is especially important to reduce communication data for mobile medical applications. However, reconstructing the signal from CS is usually left off-line due to the complex computations. In this paper, we integrate two key technologies to enable on-line energy-efficient CS signal reconstruction. These are (1) the use of Bayesian CS Belief Propagation (CS-BP) as the algorithm basis and (2) the novel design of stochastic computing (SC) circuits to efficiently map CS-BP algorithm. The overall signal reconstruction system is implemented with digital SC circuits in 65nm CMOS and recovers compressively sensed electrocardiography (ECG) and electromyography (EMG) signals with 11X to 8X data compression factor. Compared to a conventional binary design, post-layout simulation results show that the proposed stochastic design performs reconstruction with 5X energy-delay product improvement and 2X area reduction.
UR - http://www.scopus.com/inward/record.url?scp=84962456201&partnerID=8YFLogxK
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U2 - 10.1109/ICCD.2015.7357144
DO - 10.1109/ICCD.2015.7357144
M3 - Conference contribution
AN - SCOPUS:84962456201
T3 - Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015
SP - 443
EP - 446
BT - Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd IEEE International Conference on Computer Design, ICCD 2015
Y2 - 18 October 2015 through 21 October 2015
ER -