Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits

Yufei Ma; Minkyu Kim; Yu Cao; Jae Sun Seo; Sarma Vrudhula

doi:10.1109/ICCD.2015.7357144

Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits

Yufei Ma, Minkyu Kim, Yu Cao, Jae Sun Seo, Sarma Vrudhula

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	443-446
Number of pages	4
ISBN (Electronic)	9781467371650
DOIs	https://doi.org/10.1109/ICCD.2015.7357144
State	Published - Dec 14 2015
Externally published	Yes
Event	33rd IEEE International Conference on Computer Design, ICCD 2015 - New York City, United States Duration: Oct 18 2015 → Oct 21 2015

Publication series

Name	Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015

Other

Other	33rd IEEE International Conference on Computer Design, ICCD 2015
Country/Territory	United States
City	New York City
Period	10/18/15 → 10/21/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICCD.2015.7357144

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Ma, Y., Kim, M., Cao, Y., Seo, J. S., & Vrudhula, S. (2015). Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits. In Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015 (pp. 443-446). Article 7357144 (Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCD.2015.7357144

Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits. / Ma, Yufei; Kim, Minkyu; Cao, Yu et al.
Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 443-446 7357144 (Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ma, Y, Kim, M, Cao, Y, Seo, JS & Vrudhula, S 2015, Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits. in Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015., 7357144, Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015, Institute of Electrical and Electronics Engineers Inc., pp. 443-446, 33rd IEEE International Conference on Computer Design, ICCD 2015, New York City, United States, 10/18/15. https://doi.org/10.1109/ICCD.2015.7357144

Ma Y, Kim M, Cao Y, Seo JS, Vrudhula S. Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits. In Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 443-446. 7357144. (Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015). doi: 10.1109/ICCD.2015.7357144

Ma, Yufei ; Kim, Minkyu ; Cao, Yu et al. / Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits. Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 443-446 (Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015).

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title = "Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits",

abstract = "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.",

author = "Yufei Ma and Minkyu Kim and Yu Cao and Seo, {Jae Sun} and Sarma Vrudhula",

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AU - Vrudhula, Sarma

PY - 2015/12/14

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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.

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Energy-efficient reconstruction of compressively sensed bioelectrical signals with stochastic computing circuits

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