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

研究成果: 書貢獻/報告類型 › 會議貢獻

16 引文斯高帕斯（Scopus）

摘要

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.

原文	英語
主出版物標題	IEEE Symposium on VLSI Circuits, Digest of Technical Papers
出版狀態	已發佈 - 2013
事件	2013 Symposium on VLSI Circuits, VLSIC 2013 - Kyoto, 日本持續時間: 6月 12 2013 → 6月 14 2013

其他

其他	2013 Symposium on VLSI Circuits, VLSIC 2013
國家/地區	日本
城市	Kyoto
期間	6/12/13 → 6/14/13

ASJC Scopus subject areas

電氣與電子工程
電子、光磁材料

其它文件與鏈接

Link to publication in Scopus

引用此

@inproceedings{8c957546146a42f99ab30f67aade3bc7,

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",

}

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

摘要

其他

ASJC Scopus subject areas

其它文件與鏈接

指紋

引用此