Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures

Ian A Trawinski, Hayden Wimmer, Weitian Tong

Research output: Contribution to journalArticlepeer-review

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.

Disciplines

  • Computer Sciences

Keywords

  • Bitcoin
  • Cryptocurrency
  • Deep Learning
  • LSTM
  • RNN

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