This comprehensive review summarizes state of the art, challenges, and prospects of the
neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the …

The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …

Recent advances in deep learning have shown that Binary Neural Networks (BNNs) are
capable of providing a satisfying accuracy on various image datasets with significant …

As data movement operations and power-budget become key bottlenecks in the design of
computing systems, the interest in unconventional approaches such as processing-in …

Spiking neural networks (SNNs) are an active research domain toward energy-efficient
machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs …

We present an analog-integrated circuit implementation of long short-term memory network,
which is compatible with digital CMOS technology. We have used multiple-input floating …

Although recent advances in resistive random access memory (ReRAM)-based accelerator
designs for deep convolutional neural networks (CNNs) offer energy-efficiency …

With analog current accumulation feature, resistive memory (ReRAM) crossbars are widely
studied to accelerate neural network applications. The ReRAM crossbar based accelerators …

Binary Neural Network (BNN) has a great potential to be implemented on Resistive memory
Crossbar Array (RCA)-based hardware accelerators because it requires only 1-bit precision …

Binary Neural Networks (BNN) have binarized (-1 and 1) weights and feature maps.
Achieving smaller model sizes and computational simplicity, they are well suited for edge-AI …