Thinking Machines (eBook)

Machine Learning and Its Hardware Implementation

Shigeyuki Takano (Autor)

eBook Download: PDF | EPUB

2021 | 1. Auflage
322 Seiten
Elsevier Science (Verlag)
978-0-12-818280-2 (ISBN)

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

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.

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

Erscheint lt. Verlag	27.3.2021
Sprache	englisch
Themenwelt	Mathematik / Informatik ► Informatik ► Theorie / Studium
ISBN-10	0-12-818280-6 / 0128182806
ISBN-13	978-0-12-818280-2 / 9780128182802

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