A low-power microprocessor for data-driven analysis of analytically- intractable physiological signals in advanced medical sensors

Kyong Ho Lee, Naveen Verma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Data-driven methods based on machine learning enable powerful frameworks for analyzing complex physiological signals in medical-sensor applications; however, these methods are not well supported by traditional DSPs. A general-purpose microprocessor is presented in 130nm CMOS that integrates configurable accelerators, enabling low-energy hardware to support the broadest range of machine-learning frameworks reported to date. In addition to computational energy, memory limitations due to the high-order data-driven models are overcome by an embedded compression/decompression accelerator, which reduces the memory footprint by 4× with overhead <8%. Using six medical applications with real clinical data, overall energy savings of 3.1-497× are demonstrated with the accelerator-based architecture.

Original languageEnglish (US)
Title of host publication2013 Symposium on VLSI Circuits, VLSIC 2013 - Digest of Technical Papers
StatePublished - Sep 17 2013
Event2013 Symposium on VLSI Circuits, VLSIC 2013 - Kyoto, Japan
Duration: Jun 12 2013Jun 14 2013

Publication series

NameIEEE Symposium on VLSI Circuits, Digest of Technical Papers

Other

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

All Science Journal Classification (ASJC) codes

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

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