Edge AI Convergence of Edge Computing and Artificial Intelligence /
Wang, Xiaofei.
Edge AI Convergence of Edge Computing and Artificial Intelligence / [electronic resource] : by Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen. - 1st ed. 2020. - XVII, 149 p. 38 illus., 34 illus. in color. online resource.
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.
9789811561863
10.1007/978-981-15-6186-3 doi
Artificial intelligence.
Computer networks .
Computer engineering.
Artificial Intelligence.
Computer Communication Networks.
Computer Engineering and Networks.
Q334-342 TA347.A78
006.3
Edge AI Convergence of Edge Computing and Artificial Intelligence / [electronic resource] : by Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen. - 1st ed. 2020. - XVII, 149 p. 38 illus., 34 illus. in color. online resource.
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.
9789811561863
10.1007/978-981-15-6186-3 doi
Artificial intelligence.
Computer networks .
Computer engineering.
Artificial Intelligence.
Computer Communication Networks.
Computer Engineering and Networks.
Q334-342 TA347.A78
006.3