University of Oulu

Aaron Yi Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, Schahram Dustdar, Thomas Hiessl, Dieter Kranzlmüller, Madhusanka Liyanage, Setareh Maghsudi, Nitinder Mohan, Jörg Ott, Jan S. Rellermeyer, Stefan Schulte, Henning Schulzrinne, Gürkan Solmaz, Sasu Tarkoma, Blesson Varghese, and Lars Wolf. 2022. Roadmap for edge AI: a Dagstuhl perspective. SIGCOMM Comput. Commun. Rev. 52, 1 (January 2022), 28–33.

Roadmap for edge AI : a Dagstuhl perspective

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Author: Ding, Aaron Yi1; Peltonen, Ella2; Meuser, Tobias3;
Organizations: 1TU Delft
2University of Oulu
3TU Darmstadt
4University of Vienna
5University of Mannheim
6TU Wien
7LMU Munich
8University College Dublin
9University of Tübingen
10TU Munich
11Leibniz University Hannover
12Hamburg University of Technology
13Columbia University
14NEC Labs Europe
15University of Helsinki
16University of St Andrews
17TU Braunschweig
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Association for Computing Machinery, 2022
Publish Date: 2022-10-04


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.

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Series: Computer communication review
ISSN: 0146-4833
ISSN-E: 1943-5819
ISSN-L: 0146-4833
Volume: 52
Issue: 1
Pages: 28 - 33
DOI: 10.1145/3523230.3523235
Type of Publication: B1 Journal article
Field of Science: 113 Computer and information sciences
Funding: The discussions leading to this editorial were initiated in Dagstuhl Seminar 21342 on Identifying Key Enablers in Edge Intelligence, and we thank all participants for their contributions. The work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101021808, by CHIST-ERA grant CHIST-ERA-19-CES-005, and by the Austrian Science Fund (FWF): I 5201-N.
Copyright information: © Authors 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGCOMM Computer Communication Review,