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Thinking machines : machine learning and its hardware implementation / Shigeyuki Takano, Faculty of Computer Science and Engineering, Keio University, Kanagawa, Japan.

By: Takano, Shigeyuki [author.].
Publisher: London, United Kingdom ; San Diego, CA, USA : Academic Press, [2021]Description: xxiv, 298 pages : illustrations ; 23 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780128182796.Subject(s): Machine learning | ComputersAdditional physical formats: ebook version :: No titleDDC classification: 006.31 T13
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 Appendix 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
Summary: 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.-- Source other than the Library of Congress
Item type Current location Collection Call number Status Date due Barcode
Books Books College Library
General Circulation Section 		GC GC 006.31 T13 2021 (Browse shelf) Available HNU004282

First published in Japan 2017 by Impress R&D.

Includes bibliographical references and index.

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 Appendix 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

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.-- Source other than the Library of Congress

College of Engineering and Computer Studies Bachelor of Science in Computer Engineering

College of Engineering and Computer Studies Bachelor of Science in Information Technology

Text in English

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