AI Computing Systems
An Application Driven Perspective
- 1st Edition - October 12, 2022
- Authors: Yunji Chen, Ling Li, Wei Li, Qi Guo, Zidong Du, Zichen Xu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 3 9 9 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 3 9 8 - 6
AI Computing Systems: An Application Driven Perspective adopts the principle of "application-driven, full-stack penetration" and uses the specific intelligent application of "image… Read more
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Request a sales quoteAI 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
Graduate level students taking advanced artificial intelligence courses within computer science and computer engineering; AI researchers
- Cover image
- Title page
- Table of Contents
- Copyright
- Biographies
- Preface for the English version
- Preface
- Motivation for this book
- Value of an AI computing systems course
- Content of the AI computing systems course
- Writing of this book
- Chapter 1: Introduction
- 1.1. Artificial intelligence
- 1.2. AI computing systems
- 1.3. A driving example
- 1.4. Summary
- Exercises
- References
- Chapter 2: Fundamentals of neural networks
- 2.1. From machine learning to neural networks
- 2.2. Neural network training
- 2.3. Neural network design: the principle
- 2.4. Overfitting and regularization
- 2.5. Cross-validation
- 2.6. Summary
- Exercises
- References
- Chapter 3: Deep learning
- 3.1. Convolutional neural networks for image processing
- 3.2. CNN-based classification algorithms
- 3.3. CNN-based object detection algorithms
- 3.4. Sequence models: recurrent neural networks
- 3.5. Generative adversarial networks
- 3.6. Driving example
- 3.7. Summary
- Exercises
- References
- Chapter 4: Fundamentals of programming frameworks
- 4.1. Necessities of programming frameworks
- 4.2. Fundamentals of programming frameworks
- 4.3. TensorFlow: model and tutorial
- 4.4. Deep learning inference in TensorFlow
- 4.5. Deep learning training in TensorFlow
- 4.6. Summary
- Exercises
- References
- Chapter 5: Programming framework principles
- 5.1. TensorFlow design principles
- 5.2. TensorFlow computational graph mechanism
- 5.3. TensorFlow system implementation
- 5.4. Programming framework comparison
- 5.5. Summary
- Exercises
- References
- Chapter 6: Deep learning processors
- 6.1. Deep learning processors (DLPs)
- 6.2. Deep learning algorithm analysis
- 6.3. DLP architecture
- 6.4. *Optimization design
- 6.5. Performance evaluation
- 6.6. Other accelerators
- 6.7. Summary
- Exercises
- References
- Chapter 7: Architecture for AI computing systems
- 7.1. Single-core deep learning processor
- 7.2. The multicore deep learning processor
- 7.3. Summary
- Exercises
- Chapter 8: AI programming language for AI computing systems
- 8.1. Necessity of AI programming language
- 8.2. Abstraction of AI programming language
- 8.3. Programming models
- 8.4. Fundamentals of AI programming language
- 8.5. Programming interface of AI applications
- 8.6. Debugging AI applications
- 8.7. Optimizing AI applications
- 8.8. System development on AI programming language
- 8.9. Exercises
- References
- Chapter 9: Practice: AI computing systems
- 9.1. Basic practice: image style transfer
- 9.2. Advanced practice: object detection
- 9.3. Extended practices
- Appendix A: Fundamentals of computer architecture
- A.1. The instruction set of general-purpose CPUs
- A.2. Memory hierarchy in computing systems
- References
- Appendix B: Experimental environment
- B.1. Cloud platform
- B.2. Development board
- References
- References
- Final words
- Index
- No. of pages: 600
- Language: English
- Edition: 1
- Published: October 12, 2022
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780323953993
- eBook ISBN: 9780323953986
YC
Yunji Chen
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).
Affiliations and expertise
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaLL
Ling Li
Ling Li is a full Professor at Institute of Software, Chinese Academy of Sciences. Her research interests include intelligent computing and video processing. She has coauthored two books and over 50 papers on journals and conferences (including TC, TPDS, TIP, ISCA, MICRO). She has received the Best Paper Award of MICRO’14 .
Affiliations and expertise
Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, ChinaWL
Wei Li
Wei Li is an associate professor at Institute of Computing Technology, Chinese Academy of Sciences. Her research covers high-performance intelligent computing system. She has participated in several National Key R&D Projects, etc. As a core member, she has participated in the R&D of several deep learning processors. She has published around 30 academic papers, and applied for nearly 20 patents.
Affiliations and expertise
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaQG
Qi Guo
Qi Guo is a full professor at Institute of Computing Technology, Chinese Academy of Sciences. His research interests mainly focus on computer architecture and intelligent computing systems. He has published many papers in top-tier venues such as ISCA, MICRO, HPCA, IJCAI and ACM/IEEE Transactions.
Affiliations and expertise
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaZD
Zidong Du
Zidong Du is an associate professor at Intelligent Processor Research Center, Institute of Computing Technology, Chinese Academy of Sciences. His research interests mainly focus on novel architecture for artificial intelligence, including deep learning processors, inexact/approximate computing, neural network architecture, neuromorphic architecture. He has published over 20 top-tier computer architecture research papers, including ASPLOS, MICRO, ISCA, TC, TOCS, TCAD. For his innovative works on deep learning processors, he won the best paper award of ASPLOS’14, Distinguished Doctoral Dissertation Award of CAS (40/10000), Distinguished Doctoral Dissertation Award of China Computer Federation (10 per year).
Affiliations and expertise
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaZX
Zichen Xu
Zichen Xu received his Ph.D. degree from the Ohio State University. He is a full professor and the vice dean, School of Mathematics and Computer Science, the Nanchang University, China. His research spans in the area of data-conscious complex system and architecture, including performance analysis, system optimization, energy-aware architecture, and database system design/implementation. He is an IEEE senior member and ACM member.
Affiliations and expertise
School of Mathematics and Computer Science, The Nanchang University, Nanchang, ChinaRead AI Computing Systems on ScienceDirect