DagRep.13.10.130.pdf
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Edge-AI: Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 23432)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 13, Issue 10
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-04-08
Cite AsGet BibTex
Aaron Ding, Eyal de Lara, Schahram Dustdar, Ella Peltonen, and Tobias Meuser. Edge-AI: Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 23432). In Dagstuhl Reports, Volume 13, Issue 10, pp. 130-138, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/DagRep.13.10.130
https://doi.org/10.4230/DagRep.13.10.130
Abstract
Edge computing promises to decentralize cloud applications while providing more bandwidth and reducing latency. Based on the discussion of our first Dagstuhl Seminar and the continuation work that took place after the seminar, we continued our work on identified challenges that need to be further addressed within the community. These challenges included 1) large-scale deployment of the edge-cloud continuum, 2) energy optimization and sustainability of such large-scale AI/ML learning and modelling, and 3) trustworthiness, security, and ethical questions related to the whole continuum. In this seminar, we discussed the current state of Edge Intelligence and shaped a holistic view of its challenges and applications. The main concerns were 1) the assessment and applicability of Edge Intelligence solutions, 2) energy consumption and sustainability, and 3) the new trend of Large-Language Models.
Subject Classification
ACM Subject Classification
- Computer systems organization → Distributed architectures
- Computing methodologies → Artificial intelligence
- Networks
Keywords
- cloud computing
- edge computing
- edge intelligence
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