An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract)

Christine Mwase, Albert Njoroge Kahira, Zhuo Zou

AAAI 2024 pp. 23590-23591

doi:10.1609/AAAI.V38I21.30485 /aaai/2024/mwase2024aaai-empirical/

Abstract

Deep learning (DL), despite its success in various fields, remains expensive and inaccessible to many due to its need for powerful supercomputing and high-end GPUs. This study explores alternative computing infrastructure and methods for distributed DL on low-energy, low-cost devices. We experiment on Raspberry Pi 4 devices with ARM Cortex-A72 processors and train a ResNet-18 model on the CIFAR-10 dataset. Our findings reveal limitations and opportunities for future optimizations, paving the way for a DL toolset for low-energy edge devices.

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Text

Mwase et al. "An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30485

Markdown

[Mwase et al. "An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/mwase2024aaai-empirical/) doi:10.1609/AAAI.V38I21.30485

BibTeX

@inproceedings{mwase2024aaai-empirical,
  title     = {{An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract)}},
  author    = {Mwase, Christine and Kahira, Albert Njoroge and Zou, Zhuo},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23590-23591},
  doi       = {10.1609/AAAI.V38I21.30485},
  url       = {https://mlanthology.org/aaai/2024/mwase2024aaai-empirical/}
}