The performance of conventional computer based on von Neumann architecture is limited
due to the physical separation of memory and processor. By synergistically integrating …

Perovskite materials have driven tremendous advances in constructing electronic devices
owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Since the beginning of information processing by electronic components, the nervous
system has served as a metaphor for the organization of computational primitives. Brain …

Synaptic devices, including synaptic memristor and synaptic transistor, are emerging
nanoelectronic devices, which are expected to subvert traditional data storage and …

A multitude of chemical, biological, and material systems present an inductive behavior that
is not electromagnetic in origin. Here, it is termed a chemical inductor. We show that the …

The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

Machine learning, particularly in the form of deep learning (DL), has driven most of the
recent fundamental developments in artificial intelligence (AI). DL is based on computational …

An investigation of the kinetic behavior of MAPbI3 memristors shows that the onset voltage
to a high conducting state depends strongly on the voltage sweep rate, and the impedance …