Deep neural networks have evolved remarkably over the past few years and they are
currently the fundamental tools of many intelligent systems. At the same time, the …

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-
art performance in various Artificial Intelligence tasks. To accelerate the experimentation and …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Training of large-scale deep neural networks is often constrained by the available
computational resources. We study the effect of limited precision data representation and …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a
broad range of systems (from embedded systems to data centers). At the same time, a small …

When quantizing neural networks, assigning each floating-point weight to its nearest fixed-
point value is the predominant approach. We find that, perhaps surprisingly, this is not the …

Neural Networks presents concepts of neural-network models and techniques of parallel
distributed processing in a three-step approach:-A brief overview of the neural structure of …

This article presents a comprehensive overview of the hardware realizations of artificial
neural network (ANN) models, known as hardware neural networks (HNN), appearing in …

Deep networks are state-of-the-art models used for understanding the content of images,
videos, audio and raw input data. Current computing systems are not able to run deep …