Roadmap for edge AI: a Dagstuhl perspective
Abstract
Edge AI deploys AI methods and capabilities across edge computing resources. Edge AI adapts to data-driven applications, enhances 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 research community investigates novel ML methods and network systems for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The paper summarises the discussions of the Dagstuhl Seminar 21342.
Top- Ding, Aaron Yi
- Peltonen, Ella
- Meuser, Tobias
- Aral, Atakan
- Becker, Christian
- Dustdar, Schahram
- Hiessl, 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
Shortfacts
Category |
Journal Paper |
Divisions |
Scientific Computing |
Subjects |
Datenverarbeitungsmanagement Kuenstliche Intelligenz Rechnerperipherie, Datenkommunikationshardware Systemarchitektur Allgemeines |
Journal or Publication Title |
ACM SIGCOMM Computer Communication Review |
ISSN |
0146-4833 |
Publisher |
ACM |
Page Range |
pp. 28-33 |
Number |
1 |
Volume |
52 |
Date |
January 2022 |
Official URL |
http://dx.doi.org/10.1145/3523230.3523235 |
Export |