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.
Original language | American English |
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Journal | IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) Proceedings |
DOIs | |
State | Published - Dec 6 2021 |
Disciplines
- Computer Sciences
Keywords
- Bitcoin
- Cryptocurrency
- Deep Learning
- LSTM
- RNN