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Thinking Machines - 1st Edition - ISBN: 9780128182796

Thinking Machines

1st Edition

Machine Learning and Its Hardware Implementation

Author: Shigeyuki Takano
Paperback ISBN: 9780128182796
Imprint: Academic Press
Published Date: 1st March 2021
Page Count: 200
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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.

Key Features

  • 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


Graduate students and researchers in computer science/computer engineering, neural engineering, model prediction

Table of Contents

1. Introduction
2. Traditional Microarchitectures
3. Machine Learning and its Implementation
4. Applications, ASICs, and Domain-Specific Architectures
5. Machine Learning Model Development
6. Performance Improvement Methods
7. Study of Hardware Implementation
8. Keys of Hardware Implementation
9. Conclusion

A. Basics of Deep Learning
B. Modeling of Deep Learning Hardware
C. Advanced Network Models
D. National Trends for Research and Its Investment
E. Machine Learning and Social


No. of pages:
© Academic Press 2021
1st March 2021
Academic Press
Paperback ISBN:

About the Author

Shigeyuki Takano

Shigeyuki Takano developed digital signal processors at Sanyo Semiconductor. At Dowango, he prototyped a hardware video transcoder, and discovered the power of deep learning; he read deep learning books and found that deep learning tasks can be implemented as hardware. He is currently an engineer a leading automotove company where he worked on the development of an in-house deep learning processor. He is currently pursuing a PhD in Computer Engineering at Keio University, Kanagawa. His interests include processor architecture, especially for domain-specific architectures.

Affiliations and Expertise

Keio University, Kanagawa, Japan

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