Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

The co-integration of logic switches and neuromorphic functions could be used to create
new computing architectures with low power consumption and novel functionalities. Two …

An electronic skin (e-skin) for the next generation of robots is expected to have biological
skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is …

Neural-network training can be slow and energy intensive, owing to the need to transfer the
weight data for the network between conventional digital memory chips and processor chips …

A memristor is a resistive device with an inherent memory. The theoretical concept of a
memristor was connected to physically measured devices in 2008 and since then there has …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

This comprehensive review summarizes state of the art, challenges, and prospects of the
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

The memory requirement of at-scale deep neural networks (DNN) dictate that synaptic
weight values be stored and updated in off-chip memory such as DRAM, limiting the energy …