The number of nodes typically used in sensory networks is growing rapidly, leading to large
amounts of redundant data being exchanged between sensory terminals and computing …

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

Federated Learning (FL) has emerged as a promising technique for edge devices to
collaboratively learn a shared prediction model, while keeping their training data on the …

Most existing Augmented Reality (AR) and Mixed Reality (MR) systems are able to
understand the 3D geometry of the surroundings but lack the ability to detect and classify …

Constructing a computing circuit in three dimensions (3D) is a necessary step to enable the
massive connections and efficient communications required in complex neural networks. 3D …

Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

Convolutional neural networks (CNNs) excel in a wide variety of computer vision
applications, but their high performance also comes at a high computational cost. Despite …

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 …

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 …

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 …