A Multifault‐Tolerant Training Scheme for Nonideal Memristive Neural Networks

A Multifault‐Tolerant Training Scheme for Nonideal Memristive Neural Networks

Memristor crossbar is extensively investigated as an energy‐efficient accelerator for neural network (NN) computations. However, hardware implementation of NNs using realistic memristors is challenging due to the ubiquity of faults (mainly classified into hard and soft faults) in memristors. Herein,...

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Bibliografiska uppgifter
Huvudupphovsmän:	Yihong Chen, Zhen Fan, Shuai Dong, Minghui Qin, Min Zeng, Xubing Lu, Gougu Zhou, Xingsen Gao, Jun-Ming Liu
Materialtyp:	Artikel
Språk:	English
Publicerad:	Wiley 2022-05-01
Serie:	Advanced Intelligent Systems
Ämnen:	fault tolerance Manhattan update rule memristive neural network multi-fault detection targeted weight pruning
Länkar:	https://doi.org/10.1002/aisy.202100237

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