Hardware-friendly stochastic and adaptive learning in memristor convolutional neural networks

• 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: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Convolutional Neural Networks; Memristors
• Online Access: https://hdl.handle.net/10356/159293
• Institution: Nanyang Technological University

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...