Journal
NEURAL NETWORKS
Volume 131, Issue -, Pages 300-311Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2020.07.035
Keywords
Memristor; Memristor-based neural network; Recurrent neural network; Long short-term memory; Sequence data processing
Funding
- National Key Research and Development Program of China [2016YFB0800402]
- Innovation Group Project of the National Natural Science Foundation of China [61821003]
- National Natural Science Foundation of China [61673188, 61761130081]
- Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
- 111 Project on Computational Intelligence and Intelligent Control [B18024]
- Missouri University of Science and Technology Mary K. Finley Endowment and Intelligent Systems Center, USA
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Artificial neural networks (ANNs), such as the convolutional neural network (CNN) and long short-term memory (LSTM), have high complexity and contain large numbers of parameters. Memristor-based neural networks, which have the ability of in-memory and parallel computing, are therefore proposed to accelerate the operations of ANNs. In this paper, a memristor-based hardware realization of long short-term memory (LSTM) network with in situ training is presented. The designed memristor-based LSTM (MbLSTM) network is composed of memristor-based LSTM cell and memristor-based dense layer. Sigmoid and tanh (hyperbolic tangent) activation functions are approximately implemented through intentionally designing circuit parameters. A weight update scheme with row-parallel characteristic is put forward to update the conductance of memristors in crossbars. The highlights of MbLSTM include an effective hardware-based inference process and in situ training. The validity of MbLSTM is substantiated through classification tasks. The robustness of MbLSTM to conductance variations is also analyzed. (C) 2020 Elsevier Ltd. All rights reserved.
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