Title
Roadmap for edge AI: A Dagstuhl Perspective
Author
Ding, Aaron Yi (TU Delft Information and Communication Technology) 
Peltonen, Ella (University of Oulu)
Meuser, Tobias (Technische Universität Darmstadt)
Aral, Atakan (University of Vienna)
Becker, Christian (University of Mannheim)
Dustdar, Schahram (Technische Universität Wien)
Hiessl, Thomas (Technische Universität Wien)
Kranzlmüller, Dieter (Ludwig Maximilians University)
Liyanage, Madhusanka (University College Dublin)
Maghsudi, Setareh (Eberhard Karls Universität Tübingen)
Mohan, Nitinder (Technische Universität München)
Ott, Jörg (Technische Universität München)
Rellermeyer, Jan S. (TU Delft Data-Intensive Systems; Leibniz Universität)
Schulte, Stefan (Hamburg University of Technology)
Schulzrinne, Henning (Columbia University)
Solmaz, Gürkan (NEC Laboratories Europe)
Tarkoma, Sasu (Viikki Biocenter 1)
Varghese, Blesson (University of St Andrews)
Wolf, Lars (Technical University of Braunschweig)
Date
2022
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.
Subject
5G Beyond
Edge AI
Edge Computing
Future Cloud
Roadmap
To reference this document use:
http://resolver.tudelft.nl/uuid:b9608511-c9ea-4d78-b828-cd58871bf695
DOI
https://doi.org/10.1145/3523230.3523235
ISSN
0146-4833
Source
Computer Communications Review, 52 (1), 28-33
Part of collection
Institutional Repository
Document type
journal article
Rights
© 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