A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring

Shu Yu Hsu; Yingchieh Ho; Po Yao Chang; Pei Yu Hsu; Chien Ying Yu; Yuhwai Tseng; Tze Zheng Yang; Ten Fang Yang; Ray Jade Chen; Chauchin Su; Chen Yi Lee

A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring

Shu Yu Hsu
, Yingchieh Ho
, Po Yao Chang
, Pei Yu Hsu
, Chien Ying Yu
, Yuhwai Tseng
, Tze Zheng Yang
, Ten Fang Yang
, Ray Jade Chen
, Chauchin Su
, Chen Yi Lee

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

Abstract

A machine-learning (ML) assisted cardiac sensor SoC (CS-SoC) is designed for healthcare monitoring with mobile devices. The architecture realizes the cardiac signal acquisition with versatile feature extractions and classifications, enabling higher order analysis over traditional DSPs. Besides, the dynamic standby controller further suppresses the leakage power dissipation. Implemented in 90nm CMOS, the CS-SoC dissipates 48.6/105.2μW at 0.5-1.0V for real-time arrhythmia/myocardial infarction syndrome detection with 95.8/99% accuracy.

Original language	English
Title of host publication	2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	C252-C253
ISBN (Print)	9784863483484
Publication status	Published - 2013
Event	27th Annual Symposium on VLSI Circuits, VLSIC 2013 - Kyoto, Japan Duration: Jun 12 2013 → Jun 14 2013

Publication series

Name	IEEE Symposium on VLSI Circuits, Digest of Technical Papers
ISSN (Print)	2158-5601
ISSN (Electronic)	2158-5636

Conference

Conference	27th Annual Symposium on VLSI Circuits, VLSIC 2013
Country/Territory	Japan
City	Kyoto
Period	6/12/13 → 6/14/13

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Cite this

Hsu, S. Y., Ho, Y., Chang, P. Y., Hsu, P. Y., Yu, C. Y., Tseng, Y., Yang, T. Z., Yang, T. F., Chen, R. J., Su, C., & Lee, C. Y. (2013). A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring. In 2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers (pp. C252-C253). Article 6578683 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers). Institute of Electrical and Electronics Engineers Inc..

A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring. / Hsu, Shu Yu; Ho, Yingchieh; Chang, Po Yao et al.
2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc., 2013. p. C252-C253 6578683 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers).

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

Hsu, SY, Ho, Y, Chang, PY, Hsu, PY, Yu, CY, Tseng, Y, Yang, TZ, Yang, TF, Chen, RJ, Su, C & Lee, CY 2013, A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring. in 2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers., 6578683, IEEE Symposium on VLSI Circuits, Digest of Technical Papers, Institute of Electrical and Electronics Engineers Inc., pp. C252-C253, 27th Annual Symposium on VLSI Circuits, VLSIC 2013, Kyoto, Japan, 6/12/13.

Hsu SY, Ho Y, Chang PY, Hsu PY, Yu CY, Tseng Y et al. A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring. In 2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc. 2013. p. C252-C253. 6578683. (IEEE Symposium on VLSI Circuits, Digest of Technical Papers).

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abstract = "A machine-learning (ML) assisted cardiac sensor SoC (CS-SoC) is designed for healthcare monitoring with mobile devices. The architecture realizes the cardiac signal acquisition with versatile feature extractions and classifications, enabling higher order analysis over traditional DSPs. Besides, the dynamic standby controller further suppresses the leakage power dissipation. Implemented in 90nm CMOS, the CS-SoC dissipates 48.6/105.2μW at 0.5-1.0V for real-time arrhythmia/myocardial infarction syndrome detection with 95.8/99\% accuracy.",

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A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring

Abstract

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ASJC Scopus subject areas

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