Thinking Machines

Machine Learning and Its Hardware Implementation

Shigeyuki Takano

• Publisher: Academic Press
• Year: 2021
• ISBN:
  9780128182802
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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.

• Presents a clear understanding of various available machine learning hardware accelerator solutions that can be applied to selected machine learning algorithms
• Offers key insights into the development of hardware, from algorithms, software, logic circuits, to hardware accelerators
• Introduces the baseline characteristics of deep neural network models that should be treated by hardware as well
• Presents readers with a thorough review of past research and products, explaining how to design through ASIC and FPGA approaches for target machine learning models
• Surveys current trends and models in neuromorphic computing and neural network hardware architectures
• Outlines the strategy for advanced hardware development through the example of deep learning accelerators

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of figures
• List of tables
• Biography
• Preface
• Acknowledgments
• Outline
• Chapter 1: Introduction
• Chapter 2: Traditional microarchitectures
• Chapter 3: Machine learning and its implementation
• Chapter 4: Applications, ASICs, and domain-specific architectures
• Chapter 5: Machine learning model development
• Chapter 6: Performance improvement methods
• Chapter 7: Case study of hardware implementation
• Chapter 8: Keys to hardware implementation
• Chapter 9: Conclusion
• Appendix A: Basics of deep learning
• Appendix B: Modeling of deep learning hardware
• Appendix C: Advanced network models
• Appendix D: National research and trends and investment
• Appendix E: Machine learning and social
• Bibliography
• Index