Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-
art algorithms in Machine Learning (ML), speech recognition, computer vision, natural …

Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed
and ultra-low energy consumption because of their binary and event-driven nature …

Bayesian Neural Networks (BNNs) have been proposed to address the problem of model
uncertainty in training and inference. By introducing weights associated with conditioned …

Neuromorphic computing was originally referred to as the hardware that mimics neuro-
biological architectures to implement models of neural systems. The concept was then …

With the surge in popularity of machine learning algorithms, research has turned towards
exploring novel computing architectures in order to increase performance while limiting …

Deep learning workloads, such as convolutional neural networks (CNNs) are important due
to increasingly demanding high-performance hardware acceleration. One distinguishing …

Convolutional neural networks (CNNs) have been successfully applied in artificial intelligent
systems to perform sensory processing, sequence learning, and image processing. In …

Synapse crossbar is an elementary structure in neuromorphic computing systems (NCS).
However, the limited size of crossbars and heavy routing congestion impede the NCS …

Cognitive computing is an emerging field ushered in by the synergistic confluence of
cognitive science, data science, and an array of computing technologies. Cognitive science …