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Synopsis
With the end of Moore’s Law, domain-specific architecture (DSA) has 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 domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and 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, and 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.
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About the Author
Dr. Chao Wang is a Professor with the University of Science and Technology of China, and also the Vice Dean of the School of Software Engineering. He serves as the Associate Editor of ACM TODAES and IEEE/ACM TCBB. Dr. Wang was the recipient of ACM China Rising Star Honorable Mention, and best IP nomination of DATE 2015, Best Paper Candidate of CODES+ISSS 2018. He is a senior member of ACM, senior member of IEEE, and distinguished member of CCF.
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- PublisherChapman and Hall/CRC
- Publication date2024
- ISBN 10 0367374536
- ISBN 13 9780367374532
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages416
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Hardcover. Condition: new. Hardcover. With the end of Moores Law, domain-specific architecture (DSA) has 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 domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and 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, and 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780367374532
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