Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …

Research on electronic devices and materials is currently driven by both the slowing down
of transistor scaling and the exponential growth of computing needs, which make present …

The effective mimicry of neurons is key to the development of neuromorphic electronics.
However, artificial neurons are not typically capable of operating in biological environments …

New computing technologies inspired by the brain promise fundamentally different ways to
process information with extreme energy efficiency and the ability to handle the avalanche of …

Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient
approach to training neural networks,,–. However, convolutional neural networks (CNNs) …

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …

Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …

Artificial neural networks have rapidly progressed in recent years, but are limited by the high
energy costs required to train them on digital hardware. Emerging analogue hardware, such …