Deep learning

Spiking neural network

Physics

Reservoir computing

Neuromorphic engineering

Crossbar switch

Operating system

Algorithm

Distributed computing

Computer architecture

Recurrent neural network

Memristor

Engineering

Artificial intelligence

Electronic engineering

Scale (ratio)

Overhead (engineering)

Computer science

Computation

Quantum mechanics

Artificial neural network

Telecommunications

Nature Materials

With their working mechanisms based on ion migration, the switching dynamics and electrical behaviour of memristive devices resemble those of synapses and neurons, making these devices promising candidates for brain-inspired computing. Built into large-scale crossbar arrays to form neural networks, they perform efficient in-memory computing with massive parallelism by directly using physical laws. The dynamical interactions between artificial synapses and neurons equip the networks with both supervised and unsupervised learning capabilities. Moreover, their ability to interface with analogue signals from sensors without analogue/digital conversions reduces the processing time and energy overhead. Although numerous simulations have indicated the potential of these networks for brain-inspired computing, experimental implementation of large-scale memristive arrays is still in its infancy. This Review looks at the progress, challenges and possible solutions for efficient brain-inspired computation with memristive implementations, both as accelerators for deep learning and as building blocks for spiking neural networks. Memristive devices show great potential as artificial synapses and neurons, yet brain-inspired computing can be realized only by integrating a large number of these devices into reliable arrays. This Review discusses the challenges in the integration and use in computation of large-scale memristive neural networks.

Memristive crossbar arrays for brain-inspired computing