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  5. Roadmap for Edge AI: A Dagstuhl Perspective
 
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Roadmap for Edge AI: A Dagstuhl Perspective

Author(s)
Ding, Aaron Yi  
Peltonen, Ella  
Meuser, Tobias  
Liyanage, Madhusanka  
et al.  
Uri
http://hdl.handle.net/10197/13090
Date Issued
2022-01
Date Available
2022-08-24T10:56:35Z
Abstract
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.
Sponsorship
European Commission Horizon 2020
Other Sponsorship
CHIST-ERA grant
Austrian Science Fund (FWF)
Type of Material
Journal Article
Publisher
Association for Computing Machinery (ACM)
Journal
ACM SIGCOMM Computer Communication Review
Volume
52
Issue
1
Start Page
28
End Page
33
Copyright (Published Version)
2022 the Authors
Subjects

Edge AI

Edge computing

5G beyond

Future cloud

Roadmap

DOI
10.1145/3523230.3523235
Language
English
Status of Item
Peer reviewed
ISSN
0146-4833
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Edge_Intelligence_Dagstuhl_CCR_ (1).pdf

Size

608.01 KB

Format

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Checksum (MD5)

b173a48ab54680fc42028f6a124139c0

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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