Overcoming crossbar nonidealities in binary neural networks through learning

Mohammed E. Fouda, Jongeun Lee, Ahmed M. Eltawil, Fadi Kurdahi

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

13 Scopus citations


The crossbar nonidealaties may considerably degrade the accuracy of matrix multiplication operation, which is the cornerstone of hardware accelerated neural networks. In this paper, we show that the crossbar nonidealities especially the wire resistance should be taken into consideration for accurate evaluation. We also present a simple yet highly effective way to capture the wire resistance effect for the inference and training of deep neural networks without extensive SPICE simulations. Different scenarios have been studied and used to show the efficacy of our proposed method.
Original languageEnglish (US)
Title of host publicationProceedings of the 14th IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2018
PublisherAssociation for Computing Machinery, Incacmhelp@acm.org
ISBN (Print)9781450358156
StatePublished - Jul 17 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2019-11-20


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