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 …

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

Novel computing architectures based on resistive switching memories (also known as
memristors or RRAMs) have been shown to be promising approaches for tackling the …

The development of memristive device technologies has reached a level of maturity to
enable the design and fabrication of complex and large-scale hybrid memristive …

As the era of big data approaches, conventional digital computers face increasing difficulties
in performance and power efficiency due to their von Neumann architecture. As a result …

As new neural network and neuromorphic architectures are being developed, new training
methods that operate within the constraints of the new architectures are required …

Multiple modern applications of electronics call for inexpensive chips that can perform
complex operations on natural data with limited energy. A vision for accomplishing this is …

We propose a CMOS Analog Vector-Matrix Multiplier for Deep Neural Networks,
implemented in a standard single-poly 180 nm CMOS technology. The learning weights are …

Underground pipeline network surveillance system attracts increasingly attentions recently
due to severe breakages caused by external excavation equipments in the mainland of …

Memristors are used in artificial neural networks owing to their exceptional integration
capabilities and scalability. However, traditional memristors are hampered by limited …