Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks.
This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Traditional microarchitectures - p. 19 3. Chapter 3. Machine learning and its implementation - p. 49 4. Chapter 4. Applications, ASICs, and domain-specific architectures - p. 67 5. Chapter 5. Machine learning model development - p. 93 6. Chapter 6. Performance improvement methods - p. 105 7. Chapter 7. Case study of hardware implementation - p. 151 8. Chapter 8. Keys to hardware implementation - p. 205 9. Chapter 9. Conclusion - p. page217 10. Appendix A. Basics of deep learning - p. 221 11. Appendix B. Modeling of deep learning hardware - p. 233 12. Appendix C. Advanced network models - p. 237 13. Appendix D. National research and trends and investment - p. 255 14. Appendix E. Machine learning and social - p. 259 15. Bibliography - p. 271 16. Index - p. 289
Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks.
This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Traditional microarchitectures - p. 19 3. Chapter 3. Machine learning and its implementation - p. 49 4. Chapter 4. Applications, ASICs, and domain-specific architectures - p. 67 5. Chapter 5. Machine learning model development - p. 93 6. Chapter 6. Performance improvement methods - p. 105 7. Chapter 7. Case study of hardware implementation - p. 151 8. Chapter 8. Keys to hardware implementation - p. 205 9. Chapter 9. Conclusion - p. page217 10. Appendix A. Basics of deep learning - p. 221 11. Appendix B. Modeling of deep learning hardware - p. 233 12. Appendix C. Advanced network models - p. 237 13. Appendix D. National research and trends and investment - p. 255 14. Appendix E. Machine learning and social - p. 259 15. Bibliography - p. 271 16. Index - p. 289
