Roadmap for edge AI-Reference-Cited by-同舟云学术

Roadmap for edge AI

Published:2022-01-30 Issue:1 Volume:52 Page:28-33
ISSN:0146-4833
Container-title:ACM SIGCOMM Computer Communication Review
language:en
Short-container-title:SIGCOMM Comput. Commun. Rev.

Author:

Ding Aaron Yi¹,Peltonen Ella²,Meuser Tobias³,Aral Atakan⁴,Becker Christian⁵,Dustdar Schahram⁶,Hiessl Thomas⁶,Kranzlmüller Dieter⁷,Liyanage Madhusanka⁸,Maghsudi Setareh⁹,Mohan Nitinder¹⁰,Ott Jörg¹⁰,Rellermeyer Jan S.¹¹,Schulte Stefan¹²,Schulzrinne Henning¹³,Solmaz Gürkan¹⁴,Tarkoma Sasu¹⁵,Varghese Blesson¹⁶,Wolf Lars¹⁷

Affiliation:

1. Delft University of Technology

2. University of Oulu

3. TU Darmstadt

4. University of Vienna

5. University of Mannheim

6. TU Wien

7. LMU Munich

8. University Collage Dublin

9. University of Tübingen

10. TU Munich

11. Leibniz University Hannover, Delft University of Technology

12. Hamburg University of Technology

13. Columbia University

14. NEC Labs Europe

15. University of Helsinki

16. University of St Andrews

17. TU Braunschweig

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, 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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3523230.3523235

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