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

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological
synaptic plasticity and learning are described. The material properties and electrical …

We survey progress in the PCM field over the past five years, ranging from large-scale PCM
demonstrations to materials improvements for high–temperature retention and faster …

Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but
are prone to device variability. We propose a novel neural network-based computing …

The cross-point array architecture with resistive synaptic devices has been proposed for on-
chip implementation of weighted sum and weight update in the training process of learning …

With the advent of artificial intelligence (AI), memristors have received significant interest as
a synaptic building block for neuromorphic systems, where each synaptic memristor should …

Cognitive tasks are essential for the modern applications of electronics, and rely on the
capability to perform inference. The Von Neumann bottleneck is an important issue for such …

In this paper, we reviewed the recent trends on neuromorphic computing using emerging
memory technologies. Two representative learning algorithms used to implement a …

Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire
tracks have received great interest as components of neuromorphic information processing …

This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired
unsupervised local learning rule for the online implementation of Hebb's plasticity …