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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Fundamentals of neural networks - p. 17 3. Chapter 3 Deep learning - p. 53 4. Keywords - p. 53 5. 3.1 Convolutional neural networks for image processing - p. 53 6. 3.2 CNN-based classification algorithms - p. 65 7. 3.3 CNN-based object detection algorithms - p. 85 8. 3.4 Sequence models: recurrent neural networks - p. 101 9. 3.5 Generative adversarial networks - p. 109 10. 3.6 Driving example - p. 115 11. 3.7 Summary - p. 121 12. Exercises - p. 121 13. References - p. 121 14. Chapter 4 Fundamentals of programming frameworks - p. 123 15. Chapter 5 Programming framework principles - p. 167 16. Chapter 6 Deep learning processors - p. 207 17. Chapter 7 Architecture for AI computing systems - p. 247 18. Chapter 8 AI programming language for AI computing systems - p. 271 19. Keywords - p. 271 20. 8.1 Necessity of AI programming language - p. 271 21. 8.2 Abstraction of AI programming language - p. 280 22. 8.3 Programming models - p. 284 23. 8.4 Fundamentals of AI programming language - p. 295 24. 8.5 Programming interface of AI applications - p. 303 25. 8.6 Debugging AI applications - p. 312 26. 8.7 Optimizing AI applications - p. 331 27. 8.8 System development on AI programming language - p. 346 28. 8.9 Exercises - p. 374 29. References - p. 377 30. Chapter 9 Practice: AI computing systems - p. 379 31. Appendix A Fundamentals of computer architecture - p. 397 32. Appendix B Experimental environment - p. 403 33. References - p. 407 34. Final words - p. 417 35. Index - p. 421
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Fundamentals of neural networks - p. 17 3. Chapter 3 Deep learning - p. 53 4. Keywords - p. 53 5. 3.1 Convolutional neural networks for image processing - p. 53 6. 3.2 CNN-based classification algorithms - p. 65 7. 3.3 CNN-based object detection algorithms - p. 85 8. 3.4 Sequence models: recurrent neural networks - p. 101 9. 3.5 Generative adversarial networks - p. 109 10. 3.6 Driving example - p. 115 11. 3.7 Summary - p. 121 12. Exercises - p. 121 13. References - p. 121 14. Chapter 4 Fundamentals of programming frameworks - p. 123 15. Chapter 5 Programming framework principles - p. 167 16. Chapter 6 Deep learning processors - p. 207 17. Chapter 7 Architecture for AI computing systems - p. 247 18. Chapter 8 AI programming language for AI computing systems - p. 271 19. Keywords - p. 271 20. 8.1 Necessity of AI programming language - p. 271 21. 8.2 Abstraction of AI programming language - p. 280 22. 8.3 Programming models - p. 284 23. 8.4 Fundamentals of AI programming language - p. 295 24. 8.5 Programming interface of AI applications - p. 303 25. 8.6 Debugging AI applications - p. 312 26. 8.7 Optimizing AI applications - p. 331 27. 8.8 System development on AI programming language - p. 346 28. 8.9 Exercises - p. 374 29. References - p. 377 30. Chapter 9 Practice: AI computing systems - p. 379 31. Appendix A Fundamentals of computer architecture - p. 397 32. Appendix B Experimental environment - p. 403 33. References - p. 407 34. Final words - p. 417 35. Index - p. 421
