AMSNet: Analog Memristive System Architecture for Mean-Pooling with Dropout Convolutional Neural Network

Olga Krestinskaya, Adilya Bakambekova, Alex Pappachen James

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

1 Citation (Scopus)

Abstract

This work proposes analog hardware implementation of Mean-Pooling Convolutional Neural Network (CNN) with 50% random dropout backpropagation training. We illustrate the effect of variabilities of real memristive devices on the performance of CNN, and tolerance to the input noise. The classification accuracy of CNN is approximately 93% independent on memristor variabilities and input noise. On-chip area and power consumption of analog 180nm CMOS CNN with WOx memristors are 0.09338995mm2 and 3.3992W, respectively.

Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages272-273
Number of pages2
ISBN (Electronic)9781538678848
DOIs
Publication statusPublished - Mar 1 2019
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
Duration: Mar 18 2019Mar 20 2019

Publication series

NameProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
CountryTaiwan
CityHsinchu
Period3/18/193/20/19

Keywords

  • Analog Circuits
  • CNN
  • Memristors
  • Variability

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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