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E-book
Author Wang, Xiaofei, author.

Title Edge AI : convergence of edge computing and artificial intelligence / Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen
Published Singapore : Springer, [2020]
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Description 1 online resource (156 p.)
Contents Intro -- Preface -- Acknowledgements -- Contents -- Acronyms -- Part I Introduction and Fundamentals -- 1 Introduction -- 1.1 A Brief Introduction to Edge Computing -- 1.2 Trends in Edge Computing -- 1.3 Industrial Applications of Edge Computing -- 1.4 Intelligent Edge and Edge Intelligence -- References -- 2 Fundamentals of Edge Computing -- 2.1 Paradigms of Edge Computing -- 2.1.1 Cloudlet and Micro Data Centers -- 2.1.2 Fog Computing -- 2.1.3 Mobile and Multi-Access Edge Computing (MEC) -- 2.1.4 Definition of Edge Computing Terminologies -- 2.1.5 Collaborative End-Edge-Cloud Computing
2.2 Hardware for Edge Computing -- 2.2.1 AI Hardware for Edge Computing -- 2.2.2 Integrated Commodities Potentially for Edge Nodes -- 2.3 Edge Computing Frameworks -- 2.4 Virtualizing the Edge -- 2.4.1 Virtualization Techniques -- 2.4.2 Network Virtualization -- 2.4.3 Network Slicing -- 2.5 Value Scenarios for Edge Computing -- 2.5.1 Smart Parks -- 2.5.2 Video Surveillance -- 2.5.3 Industrial Internet of Things -- References -- 3 Fundamentals of Artificial Intelligence -- 3.1 Artificial Intelligence and Deep Learning -- 3.2 Neural Networks in Deep Learning
3.2.1 Fully Connected Neural Network (FCNN) -- 3.2.2 Auto-Encoder (AE) -- 3.2.3 Convolutional Neural Network (CNN) -- 3.2.4 Generative Adversarial Network (GAN) -- 3.2.5 Recurrent Neural Network (RNN) -- 3.2.6 Transfer Learning (TL) -- 3.3 Deep Reinforcement Learning (DRL) -- 3.3.1 Reinforcement Learning (RL) -- 3.3.2 Value-Based DRL -- 3.3.3 Policy-Gradient-Based DRL -- 3.4 Distributed DL Training -- 3.4.1 Data Parallelism -- 3.4.2 Model Parallelism -- 3.5 Potential DL Libraries for Edge -- References -- Part II Artificial Intelligence and Edge Computing
4 Artificial Intelligence Applications on Edge -- 4.1 Real-time Video Analytic -- 4.1.1 Machine Learning Solution -- 4.1.2 Deep Learning Solution -- 4.1.2.1 End Level -- 4.1.2.2 Edge Level -- 4.1.2.3 Cloud Level -- 4.2 Autonomous Internet of Vehicles (IoVs) -- 4.2.1 Machine Learning Solution -- 4.2.2 Deep Learning Solution -- 4.2.2.1 End Level -- 4.2.2.2 Edge Level -- 4.2.2.3 Cloud Level -- 4.3 Intelligent Manufacturing -- 4.3.1 Machine Learning Solution -- 4.3.2 Deep Learning Solution -- 4.3.2.1 End Level -- 4.3.2.2 Edge Level -- 4.3.2.3 Cloud Level -- 4.4 Smart Home and City
4.4.1 Machine Learning Solution -- 4.4.2 Deep Learning Solution -- 4.4.2.1 End Level -- 4.4.2.2 Edge Level -- 4.4.2.3 Cloud Level -- References -- 5 Artificial Intelligence Inference in Edge -- 5.1 Optimization of AI Models in Edge -- 5.1.1 General Methods for Model Optimization -- 5.1.2 Model Optimization for Edge Devices -- 5.2 Segmentation of AI Models -- 5.3 Early Exit of Inference (EEoI) -- 5.4 Sharing of AI Computation -- References -- 6 Artificial Intelligence Training at Edge -- 6.1 Distributed Training at Edge -- 6.2 Vanilla Federated Learning at Edge -- 6.3 Communication-Efficient FL
Summary As an important enabler for changing peoples 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
Bibliography Includes bibliographical references
Notes Description based on online resource; title from digital title page (viewed on October 05, 2020)
Subject Artificial intelligence.
Edge computing.
Artificial intelligence.
Artificial intelligence.
Computer networking & communications.
Computers -- Hardware -- Network Hardware.
Computers -- Intelligence (AI) & Semantics.
Computers -- Networking -- General.
Edge computing.
Network hardware.
Genre/Form Electronic books.
Form Electronic book
Author Chen, Xu, author.
Han, Yiwen, author
Leung, Victor Chung Ming, 1955- author.
Niyato, Dusit, author.
Yan, Xueqiang, author
ISBN 9789811561863
9811561869