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Author Chabi, Djaafar ♦ Querlioz, Damien ♦ Zhao, Weisheng ♦ Klein, Jacques-Olivier
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Memristors ♦ Defect and variation tolerance ♦ Nanoscale crossbar ♦ Neural network ♦ On-chip learning ♦ Redundant design ♦ Supervised learning
Abstract Scaling beyond CMOS require a new combination of computing paradigm and new devices. In this context, memristor are often considered as best candidate to implement efficiently synapses in hardware neural networks. In this article, we analyze the impact of memristor parameter variability. We build an analytical model of the global reliability at the crossbar level. It is based on a supervised learning method with multilayer and redundancy extensions. Comparisons with Monte Carlo simulations of small neural network validate our analytical model. It can be used to extrapolate directly the reliability of large-scale neural system. Our extrapolations show that high defect rate and important parameter variability can be handle efficiency with a moderate amount of redundancy.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 10
Issue Number 1
Page Count 20
Starting Page 1
Ending Page 20


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Source: ACM Digital Library