Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

The brain has effectively proven a powerful inspiration for the development of computing
architectures in which processing is tightly integrated with memory, communication is event …

Neural networks (NNs) are effective machine learning models that require significant
hardware and energy consumption in their computing process. To implement NNs …

In recent years, deep neural networks (DNN) have demonstrated significant business impact
in large scale analysis and classification tasks such as speech recognition, visual object …

Approximate computing has recently emerged as a promising approach to energy-efficient
design of digital systems. Approximate computing relies on the ability of many systems and …

Bayesian networks (BNs) find widespread application in many real-world probabilistic
problems including diagnostics, forecasting, computer vision, etc. The basic computing …

Improvements in computing performance have significantly slowed down over the past few
years owing to the intrinsic limitations of computing hardware. However, the demand for data …

Stochastic computing (SC) is an unconventional method of computation that treats data as
probabilities. Typically, each bit of an N-bit stochastic number (SN) Xis randomly chosen to …

The topologically protected magnetic spin configurations known as Skyrmions offer
promising applications due to their stability, mobility, and localization. We emphasize how to …