Abstract
Deep learning-based services such as AI assistants and self-driving cars are of great interest in academia and industry because of their unrivaled performance. Because these services require high computing power, providing such services in mobile devices encounters several practical limitations like battery consumption, heat generation and high latency. To overcome this limitation, a mobile edge computing architecture that offloads computation has been proposed. We introduce 1) resource optimization method, 2) deep learning model optimization method, and 3) joint optimization method of resources and deep learning model as studies to support deep learning-based services under the MEC structure. In particular, joint optimization of resource and deep learning model is a promising solution to respond to dynamic environment changes of networks and devices more efficiently. At the end, we suggest further research topics to enable joint optimization of resource and deep learning model.
Original language | English |
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Title of host publication | 37th International Conference on Information Networking, ICOIN 2023 |
Publisher | IEEE Computer Society |
Pages | 652-655 |
Number of pages | 4 |
ISBN (Electronic) | 9781665462686 |
DOIs | |
State | Published - 2023 |
Event | 37th International Conference on Information Networking, ICOIN 2023 - Bangkok, Thailand Duration: 11 Jan 2023 → 14 Jan 2023 |
Publication series
Name | International Conference on Information Networking |
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Volume | 2023-January |
ISSN (Print) | 1976-7684 |
Conference
Conference | 37th International Conference on Information Networking, ICOIN 2023 |
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Country/Territory | Thailand |
City | Bangkok |
Period | 11/01/23 → 14/01/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- deep learning
- mobile edge computing
- resource allocation