Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 28-33 |
| Seitenumfang | 6 |
| Fachzeitschrift | Computer communication review |
| Jahrgang | 52 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 1 März 2022 |
Abstract
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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in: Computer communication review, Jahrgang 52, Nr. 1, 01.03.2022, S. 28-33.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Roadmap for edge AI
T2 - A Dagstuhl Perspective
AU - Ding, Aaron Yi
AU - Peltonen, Ella
AU - Meuser, Tobias
AU - Aral, Atakan
AU - Becker, Christian
AU - Dustdar, Schahram
AU - Hiessl, Thomas
AU - Kranzlmüller, Dieter
AU - Liyanage, Madhusanka
AU - Maghsudi, Setareh
AU - Mohan, Nitinder
AU - Ott, Jörg
AU - Rellermeyer, Jan S.
AU - Schulte, Stefan
AU - Schulzrinne, Henning
AU - Solmaz, Gürkan
AU - Tarkoma, Sasu
AU - Varghese, Blesson
AU - Wolf, Lars
N1 - Funding Information: The discussions leading to this editorial were initiated in Dagstuhl Seminar 21342 on Identifying Key Enablers in Edge Intelligence, and we thank all participants for their contributions. The work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101021808, by CHIST-ERA grant CHIST-ERA-19-CES-005, and by the Austrian Science Fund (FWF): I 5201-N.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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.
AB - 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.
KW - 5G Beyond
KW - Edge AI
KW - Edge Computing
KW - Future Cloud
KW - Roadmap
UR - http://www.scopus.com/inward/record.url?scp=85125850007&partnerID=8YFLogxK
U2 - 10.1145/3523230.3523235
DO - 10.1145/3523230.3523235
M3 - Article
AN - SCOPUS:85125850007
VL - 52
SP - 28
EP - 33
JO - Computer communication review
JF - Computer communication review
SN - 0146-4833
IS - 1
ER -