TY - JOUR
T1 - Roadmap for Edge AI
T2 - a Dagstuhl perspective
AU - Yi Ding, Aaron
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, Jans S.
AU - Schulte, Stefan
AU - Schulzrinne, Henning
AU - Solmaz, Gürkan
AU - Tarkoma, Sasu
AU - Varghese, Blesson
AU - Wolf, Lars
N1 - Funding: 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.
UR - https://arxiv.org/abs/2112.00616
UR - https://www.scopus.com/pages/publications/85125850007
U2 - 10.1145/3523230.3523235
DO - 10.1145/3523230.3523235
M3 - Editorial
SN - 0146-4833
VL - 52
SP - 28
EP - 33
JO - ACM SIGCOMM Computer Communication Review
JF - ACM SIGCOMM Computer Communication Review
IS - 1
ER -