Handling Stuck-at-faults in Memristor Crossbar Arrays using Matrix Transformations

Baogang Zhang, Necati Uysal, Deliang Fan, Rickard Ewetz

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

5 Scopus citations

Abstract

Matrix-vector multiplication is the dominating computational workload in the inference phase of neural networks. Memristor crossbar arrays (MCAs) can inherently execute matrix-vector multiplication with low latency and small power consumption. A key challenge is that the classification accuracy may be severely degraded by stuck-at-fault defects. Earlier studies have shown that the accuracy loss can be recovered by retraining each neural network or by utilizing additional hardware. In this paper, we propose to handle stuck-at-faults using matrix transformations. A transformation T changes a weight matrix W into a weight matrix, W = T (W ), which is more robust to stuck-at-faults. In particular, we propose a row flipping transformation, a permutation transformation, and a value range transformation. The row flipping transformation results in that stuck-off (stuck-on) faults are translated into stuck-on (stuck-off) faults. The permutation transformation maps small (large) weights to memristors stuck-off (stuck-on). The value range transformation is based on reducing the magnitude of the smallest and largest elements in the matrix, which results in that each stuck-at-fault introduces an error of smaller magnitude. The experimental results demonstrate that the proposed framework is capable of recovering 99% of the accuracy loss introduced by stuck-at-faults without requiring the neural network to be retrained.

Original languageEnglish (US)
Title of host publicationASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages474-479
Number of pages6
ISBN (Electronic)9781450360074
DOIs
StatePublished - Jan 21 2019
Externally publishedYes
Event24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 - Tokyo, Japan
Duration: Jan 21 2019Jan 24 2019

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Other

Other24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
CountryJapan
CityTokyo
Period1/21/191/24/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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