Analyzing the Impact of Memristor Variability on Crossbar Implementation of Regression Algorithms With Smart Weight Update Pulsing Techniques

Sahra Afshari, Mirembe Musisi-Nkambwe, Ivan Sanchez Esqueda

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision.

Original languageEnglish (US)
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
DOIs
StateAccepted/In press - 2022

Keywords

  • Computer architecture
  • Convergence
  • crossbar array
  • Integrated circuit modeling
  • machine learning
  • Mathematical models
  • Memristors
  • Programming
  • RRAM
  • stochastic regression.
  • Training
  • variability

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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