Common-source amplifier based analog artificial neural network classifier

Akshay Jayaraj, Imon Banerjee, Arindam Sanyal

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

4 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 using transistor level circuits and a multinomial (10 classes) classification accuracy of 0.82 is achieved on the MNIST dataset which consists of handwritten images of digits from 0-9. Use of common-source amplifier structure simplifies the ANN and results in 5X lower energy consumption than existing analog classifiers. The classifier performance is validated using Spectre and Matlab simulations.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
StatePublished - 2019
Externally publishedYes
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: May 26 2019May 29 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
Country/TerritoryJapan
CitySapporo
Period5/26/195/29/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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