Memristive LSTM Network for Sentiment Analysis

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Wen, S; Wei, H; Yang, Y; Guo, Z; Zeng, Z; Huang, T; Chen, Y

Published Date

  • March 1, 2021

Published In

Volume / Issue

  • 51 / 3

Start / End Page

  • 1794 - 1804

Electronic International Standard Serial Number (EISSN)

  • 2168-2232

International Standard Serial Number (ISSN)

  • 2168-2216

Digital Object Identifier (DOI)

  • 10.1109/TSMC.2019.2906098

Citation Source

  • Scopus