Artificial Intelligence Hardware Design

Artificial Intelligence Hardware Design
Author :
Publisher : John Wiley & Sons
Total Pages : 244
Release :
ISBN-10 : 9781119810476
ISBN-13 : 1119810477
Rating : 4/5 (477 Downloads)

Book Synopsis Artificial Intelligence Hardware Design by : Albert Chun-Chen Liu

Download or read book Artificial Intelligence Hardware Design written by Albert Chun-Chen Liu and published by John Wiley & Sons. This book was released on 2021-08-23 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE HARDWARE DESIGN Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field In Artificial Intelligence Hardware Design: Challenges and Solutions, distinguished researchers and authors Drs. Albert Chun Chen Liu and Oscar Ming Kin Law deliver a rigorous and practical treatment of the design applications of specific circuits and systems for accelerating neural network processing. Beginning with a discussion and explanation of neural networks and their developmental history, the book goes on to describe parallel architectures, streaming graphs for massive parallel computation, and convolution optimization. The authors offer readers an illustration of in-memory computation through Georgia Tech’s Neurocube and Stanford’s Tetris accelerator using the Hybrid Memory Cube, as well as near-memory architecture through the embedded eDRAM of the Institute of Computing Technology, the Chinese Academy of Science, and other institutions. Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition Perfect for hardware and software engineers and firmware developers, Artificial Intelligence Hardware Design is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.


Artificial Intelligence Hardware Design Related Books

Artificial Intelligence Hardware Design
Language: en
Pages: 244
Authors: Albert Chun-Chen Liu
Categories: Computers
Type: BOOK - Published: 2021-08-23 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

ARTIFICIAL INTELLIGENCE HARDWARE DESIGN Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Language: en
Pages: 414
Authors: Shiho Kim
Categories: Computers
Type: BOOK - Published: 2021-04-07 - Publisher: Elsevier

DOWNLOAD EBOOK

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the
Hardware-Aware Probabilistic Machine Learning Models
Language: en
Pages: 163
Authors: Laura Isabel Galindez Olascoaga
Categories: Technology & Engineering
Type: BOOK - Published: 2021-05-19 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate
Hardware for Artificial Intelligence
Language: en
Pages: 229
Authors: Alexantrou Serb
Categories: Science
Type: BOOK - Published: 2022-09-26 - Publisher: Frontiers Media SA

DOWNLOAD EBOOK

Efficient Processing of Deep Neural Networks
Language: en
Pages: 254
Authors: Vivienne Sze
Categories: Technology & Engineering
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

DOWNLOAD EBOOK

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