Roadmap for Edge AI: A Dagstuhl Perspective

• URL: http://arxiv.org/abs/2112.00616v1
• Date: Sat, 27 Nov 2021 16:48:20 GMT
• Title: Roadmap for Edge AI: A Dagstuhl Perspective
• Authors: Aaron Yi Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, Schahram Dustdar, Thomas Hiessl, Dieter Kranzlmuller, Madhusanka Liyanage, Setareh Magshudi, Nitinder Mohan, Joerg Ott, Jan S. Rellermeyer, Stefan Schulte, Henning Schulzrinne, Gurkan Solmaz, Sasu Tarkoma, Blesson Varghese, Lars Wolf
• Abstract summary: We envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.
• Score: 7.871316017033928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, optimization, 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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