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 …

Memristors have recently attracted significant interest due to their applicability as promising
building blocks of neuromorphic computing and electronic systems. The dynamic …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

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 …

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 …

Neuro-inspired architectures based on synaptic memory arrays have been proposed for on-
chip acceleration of weighted sum and weight update in machine/deep learning algorithms …

As artificial intelligence calls for novel energy-efficient hardware, neuromorphic computing
systems based on analog resistive switching memory (RSM) devices have drawn great …

Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …