Electrical engineering

Resistive random-access memory

Memory bandwidth

Inference

Crossbar switch

Memory footprint

Latency (audio)

Operating system

Recurrent neural network

Memristor

Engineering

Long short term memory

Enhanced Data Rates for GSM Evolution

Voltage

Parallel computing

Embedded system

Artificial intelligence

Electronic engineering

Edge computing

Computer science

Bottleneck

Artificial neural network

Telecommunications

Von Neumann architecture

Nature Machine Intelligence

Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units have led to major advances in artificial intelligence. However, state-of-the-art LSTM models with significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from both limited memory capacity and limited data communication bandwidth. Here we demonstrate experimentally that the synaptic weights shared in different time steps in an LSTM can be implemented with a memristor crossbar array, which has a small circuit footprint, can store a large number of parameters and offers in-memory computing capability that contributes to circumventing the ‘von Neumann bottleneck’. We illustrate the capability of our crossbar system as a core component in 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. Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.

Long short-term memory networks in memristor crossbar arrays