On-chip Learning of Multilayer Perceptron Based on Memristors with Limited Multilevel States

Yuhang Zhang, Guanghui He, Kea Tiong Tang, Guoxing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The cross-point memristor array is viewed as a promising candidate for neuromorphic computing due to its non-volatile storage and parallel computing features. However, the programming threshold and resistance fluctuation among different multilevel states restrict the capacity of weight representation and thus numerical precision. This poses great challenges for on-chip learning. This work evaluates the deterioration of learning accuracy on multilayer perceptron due to limited multilevel states and proposes stochastic 'skip-and-update' algorithm to facilitate on-chip learning with low-precision memristors.

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.
Pages11-12
Number of pages2
ISBN (Electronic)9781538678848
DOIs
StatePublished - Mar 2019
Externally publishedYes
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan, Province of China
Duration: 18 Mar 201920 Mar 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
Country/TerritoryTaiwan, Province of China
CityHsinchu
Period18/03/1920/03/19

Keywords

  • memristor
  • multilayer perceptron
  • multilevel states
  • on-chip learning

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