7.5nJ/inference CMOS Echo State Network for Coronary Heart Disease prediction

Sanjeev Tannirkulam Chandrasekaran, Imon Banerjee, Arindam Sanyal

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

Abstract

This work presents the first on-chip, mixed-signal echo state network (ESN) for early prediction of heart disease. The ESN comprises an input layer, a non-linear projection (NP) layer, and an output layer. Only the output layer of the ESN requires training. The input layer weights are time-invariant and drawn from a static binary random distribution. Thus, the proposed ESN has significantly lower trainable parameters compared to other non-linear neural networks used for similar prediction tasks. A 65nm prototype is validated with the Cleveland Heart Disease (CHD) dataset. The ESN achieves a mean accuracy of 84.6% over 5 test chips while consuming 7.5nJ energy/inference.

Original languageEnglish (US)
Title of host publicationESSDERC 2021 - IEEE 51st European Solid-State Device Research Conference, Proceedings
PublisherEditions Frontieres
Pages103-106
Number of pages4
ISBN (Electronic)9781665437479
DOIs
StatePublished - 2021
Externally publishedYes
Event51st IEEE European Solid-State Device Research Conference, ESSDERC 2021 - Virtual, Online, France
Duration: Sep 6 2021Sep 9 2021

Publication series

NameEuropean Solid-State Device Research Conference
Volume2021-September
ISSN (Print)1930-8876

Conference

Conference51st IEEE European Solid-State Device Research Conference, ESSDERC 2021
Country/TerritoryFrance
CityVirtual, Online
Period9/6/219/9/21

Keywords

  • cardiac diseases prediction
  • data fusion and medical wearable
  • echo state network
  • Machine learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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