Domain-Specific Computer Architectures for Emerging Applications: Machine Learning and Neural Networks
暫譯: 針對新興應用的領域特定計算架構：機器學習與神經網絡

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.