Hardware‐Friendly Stochastic and Adaptive Learning in Memristor Convolutional Neural Networks

Hardware‐Friendly Stochastic and Adaptive Learning in Memristor Convolutional Neural Networks

Memristors offer great advantages as a new hardware solution for neuromorphic computing due to their fast and energy‐efficient matrix vector multiplication. However, the nonlinear weight updating property of memristors makes it difficult to be trained in a neural network learning process. Several co...

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Bibliographic Details
Main Authors: Wei Zhang, Lunshuai Pan, Xuelong Yan, Guangchao Zhao, Hong Chen, Xingli Wang, Beng Kang Tay, Gaokuo Zhong, Jiangyu Li, Mingqiang Huang
Format: Article
Language:English
Published: Wiley 2021-09-01
Series:Advanced Intelligent Systems
Subjects:
convolutional neural networks
memristors
weight updating
Online Access:https://doi.org/10.1002/aisy.202100041

Internet

https://doi.org/10.1002/aisy.202100041

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