Notice
This item was automatically migrated from a legacy system. It's data has not been checked and might not meet the quality criteria of the present system.
Record link: 
http://hdl.handle.net/20.500.12708/176501
-
Title: 
Roadmap for Edge AI: A Dagstuhl Perspective
-
Citation: 
Ding, A. Y., Peltonen, E., Meuser, T., Aral, A., Becker, C., Dustdar, S., Hiessl, T., Kranzlmüller, D., Liyanage, M., Magshudi, S., Mohan, N., Ott, J., Rellermeyer, J. S., Schulte, S., Schulzrinne, H., Solmaz, G., Tarkoma, S., Varghese, B., & Wolf, L. (2021). Roadmap for Edge AI: A Dagstuhl Perspective. arXiv. https://doi.org/10.48550/arXiv.2112.00616
-
Publisher DOI: 
10.48550/arXiv.2112.00616
-
Publication Type: 
Preprint
en
Language: 
English
-
Authors: 
Ding, Aaron Yi 
Peltonen, Ella 
Meuser, Tobias 
Aral, Atakan 
Becker, Christian 
Dustdar, Schahram 
Hiessl, Thomas 
Kranzlmüller, Dieter 
Liyanage, Madhusanka 
Magshudi, Setareh 
Mohan, Nitinder 
Ott, Jörg 
Rellermeyer, Jan S. 
Schulte, Stefan 
Schulzrinne, Henning 
Solmaz, Gürkan 
Tarkoma, Sasu 
Varghese, Blesson 
Wolf, Lars 
-
Organisational Unit: 
E194-02 - Forschungsbereich Distributed Systems 
-
ArXiv ID: 
2112.00616
-
Date (published): 
2021
-
Number of Pages: 
6
-
Preprint Server: 
arXiv 
-
Keywords: 
Roadmap; Edge Computing; Edge AI; 5G Beyond; Future Cloud
en
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 wit...
en
Science Branch: 
Informatik
-
Appears in Collections:
Preprint

Show full item record

Page view(s)

175
checked on Nov 23, 2023

Download(s)

2
checked on Nov 23, 2023

Google ScholarTM

Check