Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …

In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

Many architects believe that major improvements in cost-energy-performance must now
come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their
superior accuracy but at the cost of high computational complexity. The complexity comes …

Deep learning neural networks (DNNs) have been successful in solving a wide range of
machine learning problems. Specialized hardware accelerators have been proposed to …

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 …

Convolutional neural network (CNN) has become a successful algorithm in the region of
artificial intelligence and a strong candidate for many computer vision algorithms. But the …