In this article, we review the existing analog resistive switching memory (RSM) devices and
their hardware technologies for in-memory learning, as well as their challenges and …

We survey recent progress in the use of analog memory devices to build neuromorphic
hardware accelerators for deep learning applications. After an overview of deep learning …

Energy efficient hardware implementation of artificial neural network is challenging due
the'memory-wall'bottleneck. Neuromorphic computing promises to address this challenge by …

Neuromorphic computing is a brain-inspired computing paradigm that aims to construct
efficient, low-power, and adaptive computing systems by emulating the information …

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 …

The adjustable conductance of a two‐terminal memristor in a crossbar array can facilitate
vector‐matrix multiplication in one step, making the memristor a promising synapse for …

Distinct from the conductive filament-type counterparts, the interface-type resistive switching
(RS) devices are electroforming-free and exhibit bidirectionally continuous conductance …

Brain‐inspired neuromorphic computing has attracted considerable attention due to its
potential to circumvent the “von Neumann bottleneck” and mimic human brain activity in …

Growth of data eases the way to access the world but requires increasing amounts of energy
to store and process. Neuromorphic electronics has emerged in the last decade, inspired by …

In this study, we focused on the uniformity of resistance states of Ti/TaO x/ITO devices and
the possibility of using them in neuromorphic applications under DC and pulse …