Memristor-Based LSTM Network with in Situ Training and its Applications
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.
X. Liu et al., "Memristor-Based LSTM Network with in Situ Training and its Applications," Neural Networks, vol. 131, pp. 300-311, Elsevier, Aug 2020.
The definitive version is available at https://doi.org/10.1016/j.neunet.2020.07.035
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Long Short-Term Memory; Memristor; Memristor-Based Neural Network; Recurrent Neural Network; Sequence Data Processing
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier, All rights reserved.
04 Aug 2020