Fully hardware-implemented memristor convolutional neural network

• Published in: Nature (London) Vol. 577; no. 7792; pp. 641 - 646
• Main Authors: Yao, Peng, Wu, Huaqiang, Gao, Bin, Tang, Jianshi, Zhang, Qingtian, Zhang, Wenqiang, Yang, J. Joshua, Qian, He
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.01.2020; Nature Publishing Group
• Subjects: 639/166/987; 639/925/927/1007; Accuracy; Arrays; Artificial neural networks; Design and construction; Edge computing; Efficiency; Energy efficiency; Fabrication; Hardware; Humanities and Social Sciences; Kernels; Memristors; Methods; multidisciplinary; Neural networks; Object recognition; Science; Science (multidisciplinary); Software; Training; Transistors; China

Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks 1 – 4 . However, convolutional neural networks (CNNs)—one of the most important models for image recognition 5 —have not yet been fully hardware-implemented using memristor cross...