Roadmap for edge AI

A Dagstuhl Perspective

Journal article (2022)

Authors

Ella Peltonen University of Oulu
Tobias Meuser Technische Universität Darmstadt
Atakan Aral University of Vienna
Christian Becker University of Mannheim
Schahram Dustdar Technische Universität Wien
Thomas Hiessl Technische Universität Wien
Nitinder Mohan Technische Universität München
Jan S. Rellermeyer Leibniz Universität, Data-Intensive Systems - EEMCS
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Copyright: © 2022 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, Lars Wolf
DOI: https://doi.org/10.1145/3523230.3523235
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Published Date

2022

Language

English

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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.

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