Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

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 …

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with
less diagnosis time and cost can effectively help in controlling the disease spread with the …

Today, a large number of applications depend on deep neural networks (DNN) to process
data and perform complicated tasks at restricted power and latency specifications …

Convolutional Neural Networks (CNNs) have made breakthroughs in various fields, while
the energy consumption becomes enormous. Processing-In-Memory (PIM) architectures …

The RRAM-based accelerators enable fast and energy-efficient inference for neural
networks. However, there are some requirements to deploy neural networks on RRAM …

Memristors have been considered to be more efficient than traditional Complementary Metal
Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are …

Neural network (NN) computing is energy-consuming on traditional computing systems,
owing to the inherent memory wall bottleneck of the von Neumann architecture and the …

Resistive Random-Access-Memory (ReRAM) cross-bar is a promising technique for deep
neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing …

Neural network (NN) algorithms have become the dominant tool in visual object recognition,
natural language processing, and robotics. To enhance the computational efficiency of these …