Coverart for item
The Resource Edge intelligence in the making : optimization, deep learning, and applications, Sen Lin, Zhi Zhou, Zhaofeng Zhang, Xu Chen, Junshan Zhang

Edge intelligence in the making : optimization, deep learning, and applications, Sen Lin, Zhi Zhou, Zhaofeng Zhang, Xu Chen, Junshan Zhang

Label
Edge intelligence in the making : optimization, deep learning, and applications
Title
Edge intelligence in the making
Title remainder
optimization, deep learning, and applications
Statement of responsibility
Sen Lin, Zhi Zhou, Zhaofeng Zhang, Xu Chen, Junshan Zhang
Creator
Contributor
Author
Subject
Genre
Language
eng
Summary
With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviews the background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence
Member of
Cataloging source
CaBNVSL
Citation source
  • Compendex
  • INSPEC
  • Google scholar
  • Google book search
http://library.link/vocab/creatorName
Lin, Sen
Dewey number
004.678
Illustrations
illustrations
Index
no index present
LC call number
TK5105.8857
LC item number
L564 2020eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Zhou, Zhi
  • Zhan, Zhaofeng
  • Chen, Xu
  • Zhang, Junshan
Series statement
Synthesis lectures on learning, networks, and algorithms,
Series volume
#25
http://library.link/vocab/subjectName
  • Internet of things
  • Cloud computing
  • Mobile communication systems
  • Machine learning
  • Intelligent sensors
  • Intelligent control systems
Target audience
adult
Label
Edge intelligence in the making : optimization, deep learning, and applications, Sen Lin, Zhi Zhou, Zhaofeng Zhang, Xu Chen, Junshan Zhang
Instantiates
Publication
Note
Part of: Synthesis digital library of engineering and computer science
Bibliography note
Includes bibliographical references (pages 189-211)
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
  • 1. Introduction to edge intelligence -- 1.1. Artificial intelligence -- 1.2. Edge computing -- 1.3. Edge intelligence
  • 2. Edge intelligence via model training -- 2.1. Architectures -- 2.2. Key performance indicators -- 2.3. Enabling technologies -- 2.4. Summary
  • 3. Edge intelligence via federated meta-learning -- 3.1. Introduction -- 3.2. Related work -- 3.3. Preliminaries on meta-learning -- 3.4. Federated meta-learning for achieving real-time edge intelligence -- 3.5. Performance analysis of FedML -- 3.6. Robust federated meta-learning (FedML) -- 3.7. Experiments -- 3.8. Summary
  • 4. Edge-cloud collaborative learning via distributionally robust optimization -- 4.1. Introduction -- 4.2. Basic setting for collaborating learning toward edge intelligence -- 4.3. Collaborative learning based on edge-cloud synergy of distribution uncertainty sets -- 4.4. Collaborative learning based on knowledge transfer of conditional prior distribution -- 4.5. Summary
  • 5. Hierarchical mobile-edge-cloud model training with hybrid parallelism -- 5.1. Introduction -- 5.2. Background and motivation -- 5.3. HierTrain framework -- 5.4. Problem statement of policy scheduling -- 5.5. Optimization of policy scheduling -- 5.6. Performance evaluation -- 5.7. Summary
  • 6. Edge intelligence via model inference -- 6.1. Architectures -- 6.2. Key performance indicators -- 6.3. Enabling technologies -- 6.4. Summary
  • 7. On-demand accelerating deep neural network inference via edge computing -- 7.1. Introduction -- 7.2. Background and motivation -- 7.3. Framework and design -- 7.4. Performance evaluation -- 7.5. Summary
  • 8. Applications, marketplaces, and future directions of edge intelligence -- 8.1. Applications of edge intelligence -- 8.2. Marketplace of edge intelligence -- 8.3. Future directions on edge intelligence
Control code
202009LNA025
Dimensions
unknown
Extent
1 PDF (xvii, 215 pages)
File format
multiple file formats
Form of item
online
Isbn
9781681739915
Media category
electronic
Media MARC source
isbdmedia
Other physical details
illustrations (some color).
Reformatting quality
access
Specific material designation
remote
System control number
  • (CaBNVSL)thg00082141
  • (OCoLC)1204330396
Label
Edge intelligence in the making : optimization, deep learning, and applications, Sen Lin, Zhi Zhou, Zhaofeng Zhang, Xu Chen, Junshan Zhang
Publication
Note
Part of: Synthesis digital library of engineering and computer science
Bibliography note
Includes bibliographical references (pages 189-211)
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
  • 1. Introduction to edge intelligence -- 1.1. Artificial intelligence -- 1.2. Edge computing -- 1.3. Edge intelligence
  • 2. Edge intelligence via model training -- 2.1. Architectures -- 2.2. Key performance indicators -- 2.3. Enabling technologies -- 2.4. Summary
  • 3. Edge intelligence via federated meta-learning -- 3.1. Introduction -- 3.2. Related work -- 3.3. Preliminaries on meta-learning -- 3.4. Federated meta-learning for achieving real-time edge intelligence -- 3.5. Performance analysis of FedML -- 3.6. Robust federated meta-learning (FedML) -- 3.7. Experiments -- 3.8. Summary
  • 4. Edge-cloud collaborative learning via distributionally robust optimization -- 4.1. Introduction -- 4.2. Basic setting for collaborating learning toward edge intelligence -- 4.3. Collaborative learning based on edge-cloud synergy of distribution uncertainty sets -- 4.4. Collaborative learning based on knowledge transfer of conditional prior distribution -- 4.5. Summary
  • 5. Hierarchical mobile-edge-cloud model training with hybrid parallelism -- 5.1. Introduction -- 5.2. Background and motivation -- 5.3. HierTrain framework -- 5.4. Problem statement of policy scheduling -- 5.5. Optimization of policy scheduling -- 5.6. Performance evaluation -- 5.7. Summary
  • 6. Edge intelligence via model inference -- 6.1. Architectures -- 6.2. Key performance indicators -- 6.3. Enabling technologies -- 6.4. Summary
  • 7. On-demand accelerating deep neural network inference via edge computing -- 7.1. Introduction -- 7.2. Background and motivation -- 7.3. Framework and design -- 7.4. Performance evaluation -- 7.5. Summary
  • 8. Applications, marketplaces, and future directions of edge intelligence -- 8.1. Applications of edge intelligence -- 8.2. Marketplace of edge intelligence -- 8.3. Future directions on edge intelligence
Control code
202009LNA025
Dimensions
unknown
Extent
1 PDF (xvii, 215 pages)
File format
multiple file formats
Form of item
online
Isbn
9781681739915
Media category
electronic
Media MARC source
isbdmedia
Other physical details
illustrations (some color).
Reformatting quality
access
Specific material designation
remote
System control number
  • (CaBNVSL)thg00082141
  • (OCoLC)1204330396

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