Roadmap for Edge AI: A Dagstuhl Perspective

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Title: Roadmap for Edge AI: A Dagstuhl Perspective
Authors: Ding, Aaron YiPeltonen, EllaMeuser, TobiasLiyanage, Madhusankaet al.
Permanent link: http://hdl.handle.net/10197/13090
Date: Jan-2022
Online since: 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.
Funding Details: European Commission Horizon 2020
Funding Details: 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
Keywords: Edge AIEdge computing5G beyondFuture cloudRoadmap
DOI: 10.1145/3523230.3523235
Language: en
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/
Appears in Collections:Computer Science Research Collection

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