Learning to predict IR drop with effective training for ReRAM-based neural network hardware

Sugil Lee*, Giju Jung, Mohammed E. Fouda, Jongeun Lee, Ahmed Eltawil, Fadi Kurdahi

*Corresponding author for this work

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

22 Scopus citations

Abstract

Due to the inevitability of the IR drop problem in passive ReRAM crossbar arrays, finding a software solution that can predict the effect of IR drop without the need of expensive SPICE simulations, is very desirable. In this paper, two simple neural networks are proposed as software solution to predict the effect of IR drop. These networks can be easily integrated in any deep neural network framework to incorporate the IR drop problem during training. As an example, the proposed solution is integrated in BinaryNet framework and the test validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method. In addition, the proposed solution outperforms the prior work on challenging datasets such as CIFAR10 and SVHN.

Original languageEnglish (US)
Title of host publication2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
DOIs
StatePublished - Jul 2020
Event57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings - Design Automation Conference
Volume2020-July
ISSN (Print)0738-100X

Conference

Conference57th ACM/IEEE Design Automation Conference, DAC 2020
Country/TerritoryUnited States
CityVirtual, San Francisco
Period07/20/2007/24/20

Keywords

  • 0T1R
  • Binary neural network
  • Deep neural network
  • IR drop
  • ReRAM crossbar array

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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