Noisy In-Memory Recursive Computation with Memristor Crossbars

Elsa Dupraz, Lav R. Varshney

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


This paper considers iterative dot-product computation implemented on in-memory memristor crossbar substrates. To address the case where true memristor conductance values may differ from their target values, it introduces a theoretical framework that characterizes the effect of conductance value variations on the final computation. For simple dot-products, the final computation error can be approximated by a Gaussian distribution; the mean and variance values of the corresponding Gaussian distribution are provided. For iterative dot-product computation, recursive expressions are derived for the means and variances of the successive computation outputs. Experiments verify the accuracy of the proposed analysis on both synthetic data and on images processed with memristor-based principal component analysis.

Original languageEnglish (US)
Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728164328
StatePublished - Jun 2020
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: Jul 21 2020Jul 26 2020

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
CountryUnited States
CityLos Angeles

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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