Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

The state-of-the-art approaches employ approximate computing to reduce the energy
consumption of DNN hardware. Approximate DNNs then require extensive retraining …

Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

Current research in the area of Neural Networks (NN) has resulted in performance
advancements for a variety of complex problems. Especially, embedded system applications …

Convolutional neural networks (CNN) have achieved major breakthroughs in recent years.
Their performance in computer vision have matched and in some areas even surpassed …

With the increasing computational demands of the neural networks, many hardware
accelerators for the neural networks have been proposed. Such existing neural network …

This work observes that a large fraction of the computations performed by Deep Neural
Networks (DNNs) are intrinsically ineffectual as they involve a multiplication where one of …

Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …

This work exploits the tolerance of Deep Neural Networks (DNNs) to reduced precision
numerical representations and specifically, their recently demonstrated ability to tolerate …