
AI Computing Systems
An Application Driven Perspective
Published on 2. February 2023
Book
Paperback/Softback
600 pages
978-0-323-95399-3 (ISBN)
Description
AI Computing Systems: An Application Driven Perspective adopts the principle of "application-driven, full-stack penetration" and uses the specific intelligent application of "image style migration" to provide students with a sound starting place to learn. This approach enables readers to obtain a full view of the AI computing system. A complete intelligent computing system involves many aspects such as processing chip, system structure, programming environment, software, etc., making it a difficult topic to master in a short time.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Dimensions
Height: 191 mm
Width: 236 mm
Thickness: 22 mm
Weight
904 gr
ISBN-13
978-0-323-95399-3 (9780323953993)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
10/2022
Morgan Kaufmann
€86.95
Available for download
Persons
Yunji Chen is a full professor and Deputy Director at the Institute of Computing Technology, Chinese Academy of Sciences, Beijing. He led the development of the world's first deep learning dedicated processor chip. He has published more than 100 papers in academic conferences and journals, and held more than 100 patents. He received the Best Paper Awards at top international conferences on computer architecture ASPLOS'14 and MICRO'14 (the only two so far in Asia). He was reported as a "pioneer" and "leader" of deep learning processor by Science Magazine, and was named by the MIT Technology Review as one of the world's top 35 innovators under the age of 35 (2015).
Author
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
School of Mathematics and Computer Science, The Nanchang University, Nanchang, China
Content
1. Introduction
2. Neural Networks
3. Deep Learning
4. Fundamentals of Learning Frameworks
5. Learning Framework Principles
6. Theory behind Deep Learning Processors
7. Architecture for AI Computing Systems
8. AI Programming Language for AI Computing Systems
9. AI Computing Systems Labs
2. Neural Networks
3. Deep Learning
4. Fundamentals of Learning Frameworks
5. Learning Framework Principles
6. Theory behind Deep Learning Processors
7. Architecture for AI Computing Systems
8. AI Programming Language for AI Computing Systems
9. AI Computing Systems Labs