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

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 computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

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 …

Neuromorphic computing draws inspiration from the brain to provide computing technology
and architecture with the potential to drive the next wave of computer engineering …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Biomemristor has attracted a lot of attention due to its excellent scalability, high flexibility,
easy processing and low fabrication cost. Natural biomaterial and polymer-based memristor …

The on-chip implementation of learning algorithms would speed up the training of neural
networks in crossbar arrays. The circuit level design and implementation of a back …

There are many learning modes in associative memory, but most of memristor-based Pavlov
associative memory circuits only have a single mode. A learning circuit that can realize …

With the rapid growth of computer science and big data, the traditional von Neumann
architecture suffers the aggravating data communication costs due to the separated structure …