Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows
changes in synaptic strength that make a brain capable of learning from experience. During …

Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical
way, by enabling machine learning in the industry, business, health, transportation, and …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

In the brain, learning is achieved through the ability of synapses to reconfigure the strength
by which they connect neurons (synaptic plasticity). In promising solid-state synapses called …

At the end of a rush lasting over half a century, in which CMOS technology has been
experiencing a constant and breathtaking increase of device speed and density, Moore's …

A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …

In today's era of big‐data, a new computing paradigm beyond today's von‐Neumann
architecture is needed to process these large‐scale datasets efficiently. Inspired by the …

We analyze the response of identical pulses on a filamentary resistive memory (RRAM) to
implement the synapse function in neuromorphic systems. Our findings show that the …

In this Review, memristors are examined from the frameworks of both von Neumann and
neuromorphic computing architectures. For the former, a new logic computational process …