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

• المؤلفون الرئيسيون: Zhang, Wei, Pan, Lunshuai, Yan, Xuelong, Zhao, Guangchao, Chen, Hong, Wang, Xingli, Tay, Beng Kang, Zhong, Gaokuo, Li, Jiangyu, Huang, Mingqiang
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Electrical and electronic engineering; Convolutional Neural Networks; Memristors
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/159293
• المؤسسة: 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...