Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …

The priority of synaptic device researches has been given to prove the device potential for
the emulation of synaptic dynamics and not to functionalize further synaptic devices for more …

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

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

Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous
compute intensity. Fully realizing the potential of acceleration in this domain requires …

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 …

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 …

An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …

Convolutional neural networks (CNN) provide state-of-the-art results in a wide variety of
machine learning (ML) applications, ranging from image classification to speech recognition …