Distributed Mobile Computing for Deep Learning Applications

Distributed computation is the widely used methodology to overcome challenges that the application covering multiple mobile devices mostly experiences, such as the high complexity of the computation and the resource limitation. By splitting the required computation and distribute the computation acr...

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Veröffentlicht in:International Conference on Information Networking (Online) S. 674 - 677
Hauptverfasser: Lee, Seunghyun, Jo, Haesung, Yun, Jihyeon, Joo, Changhee
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 15.01.2025
Schlagworte:
Complexity theory
Data collection
Deep learning
Distributed computing
Internet of Things
Mobile computing
Mobile handsets
Resource management
ISSN:2996-1580
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Zusammenfassung:Distributed computation is the widely used methodology to overcome challenges that the application covering multiple mobile devices mostly experiences, such as the high complexity of the computation and the resource limitation. By splitting the required computation and distribute the computation across the multiple devices, it can achieve lowered computation time and resource required per device and effective utilization in terms of the total resource management. This is risen as an appropriate approach to manage problems that recent applications with deep learning process have. Followed by the generalization of Internet of Things (IoT) and the development of data collecting technology, the deep learning process has to handle much larger dataset which makes it hard to be transferred through the network. This also leads to more complex computation that a single device may not be able to operate itself. In this paper, we consider the distributed computation applied in various fields, and how it is applied to distribute the deep learning process through observing researches studying about it.
ISSN:2996-1580
DOI:10.1109/ICOIN63865.2025.10992744