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 proceedingConference contribution

13 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 languageEnglish
Title of host publicationIEEE Symposium on VLSI Circuits, Digest of Technical Papers
Publication statusPublished - 2013
Event2013 Symposium on VLSI Circuits, VLSIC 2013 - Kyoto, Japan
Duration: Jun 12 2013Jun 14 2013

Other

Other2013 Symposium on VLSI Circuits, VLSIC 2013
CountryJapan
CityKyoto
Period6/12/136/14/13

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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    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 IEEE Symposium on VLSI Circuits, Digest of Technical Papers [6578683]