Yunji Chen, Ling Li, Wei Li, Qi Guo, Zidong Du, Zichen Xu

AI Computing Systems (eBook, ePUB)

An Application Driven Perspective

• Format: ePub

Produktbeschreibung

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.

• Provides an in-depth analysis of the underlying principles behind the use of knowledge in intelligent computing systems
• Centers around application-driven and full-stack penetration, focusing on the knowledge required to complete this application at all levels of the software and hardware technology stack
• Supporting experimental tutorials covering key knowledge points in each chapter provide practical guidance and formalization tools for developing a simple AI computing system

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 600
• Erscheinungstermin: 12. Oktober 2022
• Englisch
• ISBN-13: 9780323953986
• Artikelnr.: 66029310

Autorenporträt

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).

Inhaltsangabe

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