DagRep.11.7.76.pdf
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Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 21342)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 11, Issue 7
Volume: Dagstuhl Reports, Volume 11
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2021-12-22
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Aaron Ding, Ella Peltonen, Sasu Tarkoma, and Lars Wolf. Identifying Key Enablers in Edge Intelligence (Dagstuhl Seminar 21342). In Dagstuhl Reports, Volume 11, Issue 7, pp. 76-88, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/DagRep.11.7.76
https://doi.org/10.4230/DagRep.11.7.76
Abstract
Edge computing, a key part of the 5G networks and beyond, promises to decentralize cloud applications while providing more bandwidth and reducing latencies. The promises are delivered by moving application-specific computations between the cloud, the data-producing devices, and the network infrastructure components at the edges of wireless and fixed networks. However, the current AI/ML methods assume computations are conducted in a powerful computational infrastructure, such as a homogeneous cloud with ample computing and data storage resources available. In this seminar, we discussed and developed presumptions for a comprehensive view of AI methods and capabilities in the context of edge computing, and provided a roadmap to bring together enablers and key aspects for edge computing and applied AI/ML fields.
Subject Classification
ACM Subject Classification
- Computer systems organization → Distributed architectures
- Computing methodologies → Artificial intelligence
- Networks
Keywords
- artificial intelligence
- communication networks
- edge computing
- intelligent networking
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