Long short-term memory networks in memristor crossbars
Abstract
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of parameters, however, have a bottleneck in computing power resulting from limited memory capacity and data communication bandwidth. Here we demonstrate experimentally that LSTM can be implemented with a memristor crossbar, which has a small circuit footprint to store a large number of parameters and in-memory computing capability that circumvents the 'von Neumann bottleneck'. We illustrate the capability of our system by solving real-world problems in regression and classification, which shows that memristor LSTM is a promising low-power and low-latency hardware platform for edge inference.
Cite
@article{arxiv.1805.11801,
title = {Long short-term memory networks in memristor crossbars},
author = {Can Li and Zhongrui Wang and Mingyi Rao and Daniel Belkin and Wenhao Song and Hao Jiang and Peng Yan and Yunning Li and Peng Lin and Miao Hu and Ning Ge and John Paul Strachan and Mark Barnell and Qing Wu and R. Stanley Williams and J. Joshua Yang and Qiangfei Xia},
journal= {arXiv preprint arXiv:1805.11801},
year = {2018}
}