Optimizing Deep Learning for Edge Intelligence: Architectures, Methods, and Applications

DOI

10.2991/978-94-6463-823-3_8How to use a DOI?

Keywords

Edge Computing; Distributed Systems; Deep Learning; Model Optimization; System Architecture

Abstract

Edge computing emerged gradually after assimilating the key aspects of cloud computing. Under its prudent task distribution and proximity to users, it can achieve low latency while maintaining a reasonable level of computational capacity. When considering the integration of edge computing and deep learning, it becomes evident that its potential remains largely untapped. The existing research has not comprehensively explored the optimal methods for integrating these two fields. This review aims to bridge this gap by examining three main aspects: edge-optimized model design, hardware-aware alignment, and practical applications. It is believed that the combination of edge computing and deep learning has enormous potential to expand the scope of intelligent systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yanzhe Li
PY  - 2025
DA  - 2025/08/31
TI  - Optimizing Deep Learning for Edge Intelligence: Architectures, Methods, and Applications
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 84
EP  - 93
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_8
DO  - 10.2991/978-94-6463-823-3_8
ID  - Li2025
ER  -