  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.
