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Edge AI = convergence of edge comput...

Wang, Xiaofei.

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Edge AI = convergence of edge computing and artificial intelligence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Edge AI/ by Xiaofei Wang ... [et al.].
其他題名:	convergence of edge computing and artificial intelligence /
其他作者:	Wang, Xiaofei.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xvii, 149 p. :ill., digital ;24 cm.
內容註:	Part I. Introduction and Fundamentals -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Edge Computing -- Chapter 3. Fundamentals of Artificial Intelligence -- Part II. Artificial Intelligence and Edge Computing -- Chapter 4. Artificial Intelligence Applications on Edge -- Chapter 5. Artificial Intelligence Inference in Edge -- Chapter 6. Artificial Intelligence Training at Edge -- Chapter 7. Edge Computing for Artificial Intelligence -- Chapter 8. Artificial Intelligence for Optimizing Edge -- Part III. Challenges and Conclusions -- Chapter 9. Lessons Learned and Open Challenges -- Chapter 10. Conclusions.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-981-15-6186-3
ISBN:	9789811561863

Edge AI = convergence of edge computing and artificial intelligence /
Edge AIconvergence of edge computing and artificial intelligence /[electronic resource] :by Xiaofei Wang ... [et al.]. - Singapore :Springer Singapore :2020. - xvii, 149 p. :ill., digital ;24 cm.

Part I. Introduction and Fundamentals -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Edge Computing -- Chapter 3. Fundamentals of Artificial Intelligence -- Part II. Artificial Intelligence and Edge Computing -- Chapter 4. Artificial Intelligence Applications on Edge -- Chapter 5. Artificial Intelligence Inference in Edge -- Chapter 6. Artificial Intelligence Training at Edge -- Chapter 7. Edge Computing for Artificial Intelligence -- Chapter 8. Artificial Intelligence for Optimizing Edge -- Part III. Challenges and Conclusions -- Chapter 9. Lessons Learned and Open Challenges -- Chapter 10. Conclusions.

As an important enabler for changing people's lives, advances in artiﬁcial intelligence (AI)-based applications and services are on the rise, despite being hindered by efﬁciency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.

ISBN: 9789811561863

Standard No.: 10.1007/978-981-15-6186-3doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Edge AI = convergence of edge computing and artificial intelligence /
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505 0 $a Part I. Introduction and Fundamentals -- Chapter 1. Introduction -- Chapter 2. Fundamentals of Edge Computing -- Chapter 3. Fundamentals of Artificial Intelligence -- Part II. Artificial Intelligence and Edge Computing -- Chapter 4. Artificial Intelligence Applications on Edge -- Chapter 5. Artificial Intelligence Inference in Edge -- Chapter 6. Artificial Intelligence Training at Edge -- Chapter 7. Edge Computing for Artificial Intelligence -- Chapter 8. Artificial Intelligence for Optimizing Edge -- Part III. Challenges and Conclusions -- Chapter 9. Lessons Learned and Open Challenges -- Chapter 10. Conclusions.
520 $a As an important enabler for changing people's lives, advances in artiﬁcial intelligence (AI)-based applications and services are on the rise, despite being hindered by efﬁciency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
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