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: Zhang, Wei, Pan, Lunshuai, Yan, Xuelong, Zhao, Guangchao, Chen, Hong, Wang, Xingli, Tay, Beng Kang, Zhong, Gaokuo, Li, Jiangyu, Huang, Mingqiang
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Electrical and electronic engineering
Convolutional Neural Networks
Memristors
Online Access:https://hdl.handle.net/10356/159293

Internet

https://hdl.handle.net/10356/159293

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