Investigation of Read Disturb and Bipolar Read Scheme on Multilevel RRAM-Based Deep Learning Inference Engine

Wonbo Shim, Yandong Luo, Jae Sun Seo, Shimeng Yu

Research output: Contribution to journalArticle

Abstract

The multilevel resistive random access memory (RRAM)-based synaptic array can enable parallel computations of vector-matrix multiplication for machine learning inference acceleration; however, any conductance drift of the cell may induce an inference accuracy drop because the analog current is summed up along the column. In this article, the read disturb-induced conductance drift characteristic is statistically measured on a test vehicle based on 2-bit HfO2 RRAM array. The drift behavior of four states is empirically modeled by a vertical and lateral filament growth mechanism. Furthermore, a bipolar read scheme is proposed and tested to enhance the resilience against the read disturb. The modeled read disturb and proposed compensation scheme are incorporated into a VGG-like convolutional neural network for CIFAR-10 data set inference.

Original languageEnglish (US)
Article number9069908
Pages (from-to)2318-2323
Number of pages6
JournalIEEE Transactions on Electron Devices
Volume67
Issue number6
DOIs
StatePublished - Jun 2020

Keywords

  • In-memory computing
  • Multilevel resistive random access memory (RRAM)
  • Neural network
  • Read disturb
  • Reliability

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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