Edge AI : convergence of edge computing and artificial intelligence

著者

    • Wang, Xiaofei
    • Han, Yiwen
    • Leung, Victor C. M.
    • Niyato, Dusit
    • Yan, Xueqiang
    • Chen, Xu

書誌事項

Edge AI : convergence of edge computing and artificial intelligence

Xiaofei Wang ... [et al.]

Springer, c2020

  • : hardback

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注記

Other authors: Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen

Includes bibliographical references

内容説明・目次

内容説明

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.

目次

1: Introduction 2: Fundamentals of Edge Computing 2.1 Paradigms of Edge Computing Cloudlet and Micro Data Centers Fog Computing Mobile (Multi-access) Edge Computing (MEC) Definition of Edge Computing Terminologies Collaborative End-Edge-Cloud Computing 2.2 Hardware for Edge Computing AI Hardware for Edge Computing Integrated Commodities Potentially for Edge Nodes Edge Computing Frameworks 2.3 Virtualizing the Edge Virtualization Techniques Network Virtualization Network Slicing 3. Fundamentals of Deep Learning 3.1 Neural Networks in Deep Learning Fully Connected Neural Network (FCNN) Auto-Encoder (AE) Convolutional Neural Network (CNN) Generative Adversarial Network (GAN) Recurrent Neural Network (RNN) Transfer Learning (TL) 3.2 Deep Reinforcement Learning (DRL) Value-based DRL Policy-gradient-based DRL 3.3 Distributed DL Training 3.4 Potential DL Libraries for Edge 4. Deep Learning Applications on Edge 4.1 Real-time Video Analytic 4.2 Autonomous Internet of Vehicles (IoVs) 4.3 Intelligent Manufacturing 4.4 Smart Home and City 5. Deep Learning Inference in Edge 5.1 Optimization of DL Models in Edge General Methods for Model Optimization Model Optimization for Edge Devices 5.2 Segmentation of DL Models 5.3 Early Exit of Inference (EEoI) 5.4 Sharing of DL Computation 6. Edge Computing for Deep Learning 6.1 Edge Hardware for DL Mobile CPUs and GPUs FPGA-based Solutions 6.2 Communication and Computation Modes for Edge DL Integral Offloading Partial Offloading Vertical Collaboration Horizontal Collaboration 6.3 Tailoring Edge Frameworks for DL 6.4 Performance Evaluation for Edge DL 7. Deep Learning Training at Edge 7.1 Distributed Training at Edge 7.2 Vanilla Federated Learning at Edge 7.3 Communication-efficient FL 7.4 Resource-optimized FL 7.5 Security-enhanced FL 8. Deep Learning for Optimizing Edge 8.1 DL for Adaptive Edge Caching 8.2 DL for Optimizing Edge Task Offloading 8.3 DL for Edge Management and Maintenance Edge Communication Edge Security Joint Edge Optimization 9. Lessons Learned and Open Challenges 9.1 More Promising Applications 9.2 General DL Model for Inference 9.3 Complete Edge Architecture for DL 9.4 Practical Training Principles at Edge 9.5 Deployment and Improvement of Intelligent Edge

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