As human society enters the big data era, huge data storage and energy‐efficient data
processing are in great demand. The resistive switching device is an emerging device with …

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

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 …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

This review addresses resistive switching devices operating according to the bipolar
valence change mechanism (VCM), which has become a major trend in electronic materials …

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 …

Neuromorphic computation is one of the axes of parallel distributed processing, and
memristor-based synaptic weight is considered as a key component of this type of …

Metal–insulator transition (MIT) coupled with an ultrafast, significant, and reversible resistive
change in Mott insulators has attracted tremendous interest for investigation into next …

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 …

Machine learning has been getting attention in recent years as a tool to process big data
generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing …