Format:
1 online resource (417 pages)
Edition:
1st ed.
ISBN:
9781040031988
Content:
This book explores the latest research in high performance domain-specific computer architectures for emerging applications, including Machine Learning and Neural Networks applications. The book discusses domain specific computing architectures and considers research issues related to the state-of-the art architectures in emerging domains.
Content:
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- 1 Overview of Domain-Specific Computing -- 1.1 Background and Current Status of Domain-Specific Computing -- 1.2 Current Domain-Specific Acceleration Means and Platforms -- 1.2.1 Current Acceleration Means -- 1.2.2 Current Domain-Specific Acceleration Platforms -- 1.3 Metrics to Measure the Effectiveness of Domain-Specific Platforms -- 1.4 Content Structure of This Book -- 2 Machine Learning Algorithms and Hardware Accelerator Customization -- 2.1 Overview of Machine Learning -- 2.1.1 Introduction to Machine Learning -- 2.1.2 Classification of Machine Learning Algorithms -- 2.1.3 Machine Learning Algorithm Acceleration Focus -- 2.1.4 Commonly Used Public Data Sets for Machine Learning -- 2.2 Design of FPGA-Based Machine Learning Gas Pedals -- 2.2.1 Designing Accelerators for Specific Problems -- 2.2.2 Designing Accelerators for Specific Algorithms -- 2.2.3 Designing Accelerators for Algorithm Common Features -- 2.2.4 Designing a Generic Accelerator Framework Using Hardware Templates -- 2.3 Conclusion and Outlook -- 2.3.1 Conclusion -- 2.3.2 Outlook -- References -- 3 Hardware Accelerator Customization for Data Mining Recommendation Algorithms -- 3.1 Background on Recommendation Algorithms and Their Hardware Acceleration -- 3.2 Introduction to Collaborative Filtering Recommendation Algorithm -- 3.2.1 Collaborative Filtering Recommendation Algorithm Based on Neighborhood Model -- 3.2.2 User-Based Collaborative Filtering Recommendation Algorithm -- 3.2.3 Collaborative Filtering Recommendation Algorithm Based on Item -- 3.2.4 SlopeOne Recommendation Algorithm -- 3.3 Hardware Acceleration Principle and Method -- 3.3.1 Hardware Acceleration Principle -- 3.3.2 Commonly Used Hardware Acceleration Method.
Content:
"With the end of Moore's Law, Domain-Specific Architectures (DSA) have become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency and real-time responsiveness for the target application. DSAs often start from the domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications such as computation, memory access, communication and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications such as machine learning, data mining, neural networks, graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures. Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas"--
Note:
Description based on publisher supplied metadata and other sources
Additional Edition:
9780367374532
Additional Edition:
Erscheint auch als Druck-Ausgabe 9780367374532
Language:
English
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