MEMRISTOR-BASED LSTM NETWORK FOR TEXT CLASSIFICATION

Gang Dou, Kaixuan Zhao, Mei Guo and Jun Mou
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Gang Dou: The College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, P. R. China
Kaixuan Zhao: The College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, P. R. China
Mei Guo: The College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, P. R. China
Jun Mou: ��School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-12

Abstract: Long short-term memory (LSTM) with significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from limited memory capacity. Hardware acceleration of LSTM using memristor circuit is an effective solution. This paper presents a complete design of memristive LSTM network system. Both the LSTM cell and the fully connected layer circuit are implemented through memristor crossbars, and the 1T1R design avoids the influence of the sneak current which helps to improve the accuracy of network calculation. To reduce the power consumption, the word embedding dimensionality was reduced using the GloVe model, and the number of features in the hidden layer was reduced. The effectiveness of the proposed scheme is verified by performing the text classification task on the IMDB dataset and the hardware training accuracy reached as high as 88.58%.

Keywords: Memristor; LSTM; Circuit Design; Text Classification (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0218348X23400406

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