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

16 Citations (Scopus)

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	IEEE Symposium on VLSI Circuits, Digest of Technical Papers
Publication status	Published - 2013
Event	2013 Symposium on VLSI Circuits, VLSIC 2013 - Kyoto, Japan Duration: Jun 12 2013 → Jun 14 2013

Other

Other	2013 Symposium on VLSI Circuits, VLSIC 2013
Country/Territory	Japan
City	Kyoto
Period	6/12/13 → 6/14/13

ASJC Scopus subject areas

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Cite this

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

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.",

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

year = "2013",

language = "English",

isbn = "9784863483484",

booktitle = "IEEE Symposium on VLSI Circuits, Digest of Technical Papers",

note = "2013 Symposium on VLSI Circuits, VLSIC 2013 ; Conference date: 12-06-2013 Through 14-06-2013",

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TY - GEN

T1 - A 48.6-to-105.2μW machine-learning assisted cardiac sensor SoC for mobile healthcare monitoring

AU - Hsu, Shu Yu

AU - Ho, Yingchieh

AU - Chang, Po Yao

AU - Hsu, Pei Yu

AU - Yu, Chien Ying

AU - Tseng, Yuhwai

AU - Yang, Tze Zheng

AU - Yang, Ten Fang

AU - Chen, Ray Jade

AU - Su, Chauchin

AU - Lee, Chen Yi

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84883773104&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883773104&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84883773104

SN - 9784863483484

BT - IEEE Symposium on VLSI Circuits, Digest of Technical Papers

T2 - 2013 Symposium on VLSI Circuits, VLSIC 2013

Y2 - 12 June 2013 through 14 June 2013

ER -

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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