Roadmap for Edge AI: A Dagstuhl Perspective
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866908373634514944 |
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| author | Ding, Aaron Yi Peltonen, Ella Meuser, Tobias Aral, Atakan Becker, Christian Dustdar, Schahram Hiessl, Thomas Kranzlmuller, Dieter Liyanage, Madhusanka Magshudi, Setareh Mohan, Nitinder Ott, Joerg Rellermeyer, Jan S. Schulte, Stefan Schulzrinne, Henning Solmaz, Gurkan Tarkoma, Sasu Varghese, Blesson Wolf, Lars |
| author_facet | Ding, Aaron Yi Peltonen, Ella Meuser, Tobias Aral, Atakan Becker, Christian Dustdar, Schahram Hiessl, Thomas Kranzlmuller, Dieter Liyanage, Madhusanka Magshudi, Setareh Mohan, Nitinder Ott, Joerg Rellermeyer, Jan S. Schulte, Stefan Schulzrinne, Henning Solmaz, Gurkan Tarkoma, Sasu Varghese, Blesson Wolf, Lars |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_00616 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Roadmap for Edge AI: A Dagstuhl Perspective Ding, Aaron Yi Peltonen, Ella Meuser, Tobias Aral, Atakan Becker, Christian Dustdar, Schahram Hiessl, Thomas Kranzlmuller, Dieter Liyanage, Madhusanka Magshudi, Setareh Mohan, Nitinder Ott, Joerg Rellermeyer, Jan S. Schulte, Stefan Schulzrinne, Henning Solmaz, Gurkan Tarkoma, Sasu Varghese, Blesson Wolf, Lars Distributed, Parallel, and Cluster Computing Artificial Intelligence I.2.11 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. |
| title | Roadmap for Edge AI: A Dagstuhl Perspective |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence I.2.11 |
| url | https://arxiv.org/abs/2112.00616 |