39fJ Analog Artificial Neural Network for Breast Cancer Classification in 65nm CMOS

Ruobing Hua, Arindam Sanyal

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

1 Scopus citations

Abstract

An analog artificial neural network (ANN) classifier using a common-source amplifier based nonlinear activation function is presented in this work. A shallow ANN is designed in 65nm CMOS to perform binary classification on breast cancer dataset and identify each patient data as either benign or malignant. Use of common-source amplifier structure simplifies the ANN and results in only 39fJ/classification at 0.8V power supply and core area of only 240μm2. The classifier is trained using Matlab and validated using Spectre simulations.

Original languageEnglish (US)
Title of host publication2019 IEEE 62nd International Midwest Symposium on Circuits and Systems, MWSCAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages436-439
Number of pages4
ISBN (Electronic)9781728127880
DOIs
StatePublished - Aug 2019
Externally publishedYes
Event62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019 - Dallas, United States
Duration: Aug 4 2019Aug 7 2019

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2019-August
ISSN (Print)1548-3746

Conference

Conference62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019
Country/TerritoryUnited States
CityDallas
Period8/4/198/7/19

Keywords

  • analog AI circuit
  • artificial neural network
  • classifier
  • CMOS
  • WBCD

ASJC Scopus subject areas

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

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