Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures
Document Type
Conference Proceeding
Publication Date
12-6-2021
Publication Title
IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) Proceedings
DOI
10.1109/IEMCON53756.2021.9623132
Abstract
Deep learning has made vast advances over the recent decade due to hardware, software, and architecture improvements. Applying deep learning still has some challenges to overcome, especially when processing on GPU or in parallel. This work seeks to apply RNN and LSTM over a variety of architectures to demonstrate the challenges and techniques encountered when applying deep learning. We apply RNN and LSTM over GPU and CPU across PC, high performance computing cluster, and cloud and present our challenges and results.
Recommended Citation
Trawinski, Ian A., Hayden Wimmer, Weitian Tong.
2021.
"Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures."
IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) Proceedings: 0371-0377: IEEE Xplore.
doi: 10.1109/IEMCON53756.2021.9623132
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/179
Comments
Georgia Southern University faculty members, Hayden Wimmer and Weitian Tong co-authored Deep Learning on Cryptocurrency – Challenges Processing GPU and CPU Architectures.