Author:
Liu Xiaoyang,Zeng Zhigang
Abstract
AbstractThe paper presents memristor crossbar architectures for implementing layers in deep neural networks, including the fully connected layer, the convolutional layer, and the pooling layer. The crossbars achieve positive and negative weight values and approximately realize various nonlinear activation functions. Then the layers constructed by the crossbars are adopted to build the memristor-based multi-layer neural network (MMNN) and the memristor-based convolutional neural network (MCNN). Two kinds of in-situ weight update schemes, which are the fixed-voltage update and the approximately linear update, respectively, are used to train the networks. Consider variations resulted from the inherent characteristics of memristors and the errors of programming voltages, the robustness of MMNN and MCNN to these variations is analyzed. The simulation results on standard datasets show that deep neural networks (DNNs) built by the memristor crossbars work satisfactorily in pattern recognition tasks and have certain robustness to memristor variations.
Funder
National Natural Science Foundation of China
Innovation Group Project of the National Natural Science Foundation of China
Technology Innovation Project of Hubei Province of China
Foundation for Innovative Research Groups of Hubei Province of China
111 Project on Computational Intelligence and Intelligent Control
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Cited by
26 articles.
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