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 …

The explosive growth in data-centric artificial intelligence related applications necessitates a
radical departure from traditional von Neumann computing systems, which involve separate …

In the mammalian nervous system, various synaptic plasticity rules act, either individually or
synergistically, over wide-ranging timescales to enable learning and memory formation …

Neuromorphic computing can process large amounts of information in parallel and provides
a powerful tool to solve the von Neumann bottleneck. Constructing an artificial neural …

The information technologies have been increasing exponentially following Moore's law
over the past decades. This has fundamentally changed the ways of work and life. However …

Neuromorphic hardware that emulates biological computations is a key driver of progress in
AI. For example, memristive technologies, including chalcogenide-based in-memory …

Deployment of novel artificial synapses serves as the crucial unit for building neuromorphic
hardware to drive data‐intensive applications. Emulation of complex neural behavior …

Facing the computing demands of Internet of things (IoT) and artificial intelligence (AI), the
cost induced by moving the data between the central processing unit (CPU) and memory is …

Ferroelectric HfZrO x (Fe-HZO) with a larger remnant polarization (P r) is achieved by using
a poly-GeSn film as a channel material as compared with a poly-Ge film because of the …

The passivity, low power consumption, memory characteristics and nanometer size of
memristors make them the best choice to simulate synapses in artificial neural networks. In …