Roadmap for edge AI : a Dagstuhl perspective
Ding, Aaron Yi; Peltonen, Ella; Meuser, Tobias; Aral, Atakan; Becker, Christian; Dustdar, Schahram; Hiess, Thomas; Kranzlmüller, Dieter; Liyanage, Madhusanka; Maghsudi, Setareh; Mohan, Nitinder; Ott, Jörg; Rellermeyer, Jan S.; Schulte, Stefan; Schulzrinne, Henning; Solmaz, Gürkan; Tarkoma, Sasu; Varghese, Blesson; Wolf, Lars (2022-03-01)
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. https://doi.org/10.1145/3523230.3523235
© 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, http://dx.doi.org/10.1145/3523230.3523235.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2022100461123
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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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