4.7 Article

Memristive LSTM Network for Sentiment Analysis

Journal

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 51, Issue 3, Pages 1794-1804

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2906098

Keywords

Hardware; Memristors; Neural networks; Sentiment analysis; Task analysis; Training; Image coding; Deep learning; long short-term memory (LSTM); memristor; sentiment analysis

Funding

  1. Natural Science Foundation of China [61673187, 61673188]
  2. National Priorities Research Program (NPRP) through the Qatar National Research Fund (a member of Qatar Foundation) [NPRP 8-274-2-107]

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This paper presents a complete solution for the hardware design of a memristor-based MLSTM network, utilizing parameter sharing mechanism and efficient implementation of memristor crossbars to reduce hardware design scale. Experimental results validate the effectiveness of the system.
This paper presents a complete solution for the hardware design of a memristor-based long short-term memory (MLSTM) network. Throughout the design process, we fully consider the external and internal structures of the long short-term memory (LSTM), both of which are efficiently implemented by memristor crossbars. In the specific design of the internal structure, the parameter sharing mechanism is used between the LSTM cells to minimize the hardware design scale. In particular, we designed a circuit that requires only one memristor crossbar for each unit in the LSTM cell. The activation function, including sigmoid and tanh (hyperbolic tangent function), involved in each unit is approximated by a piecewise function, which is designed with the corresponding hardware. To verify the effectiveness of the system we designed, we test it on IMDB and SemEval datasets. Considering the huge impact of the dimensions of the input data on the scale of the hardware design, we use word2vector instead of one-hot encoding for the input data encoding. With the parameter sharing mechanism, the transformed vectors are input in different periods, so only 65 memristive crossbars are needed in the entire system to complete the sentiment analysis of the input text. The experimental results verify the effectiveness of our proposed MLSTM system.

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