Neuromorphic computers comprised of artificial neurons and synapses could provide a
more efficient approach to implementing neural network algorithms than traditional …

On metrics of density and power efficiency, neuromorphic technologies have the potential to
surpass mainstream computing technologies in tasks where real-time functionality …

Memristor-based neuromorphic computing could overcome the limitations of traditional von
Neumann computing architectures—in which data are shuffled between separate memory …

Artificial neural networks are notoriously power-and time-consuming when implemented on
conventional von Neumann computing systems. Consequently, recent years have seen an …

Memristors, acting as artificial synapses, have promised their prospects in neuromorphic
systems that imitate the brain's computing paradigm. However, most studies focused on the …

A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can provide massive …

Neuromorphic computing is an approach to efficiently solve complicated learning and
cognition problems like the human brain using electronics. To efficiently implement the …

This article provides a review of current development and challenges in brain-inspired
computing with memristors. We review the mechanisms of various memristive devices that …

Recent research in nanotechnology has led to the practical realization of nanoscale devices
that behave as memristors, a device that was postulated in the seventies by Chua based on …

Memristors are memory resistors that promise the efficient implementation of synaptic
weights in artificial neural networks. Whereas demonstrations of the synaptic operation of …