Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures

Alex Trawinski; Hayden Wimmer; Weitian Tong

doi:10.1109/IEMCON53756.2021.9623132

Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures

Alex Trawinski, Hayden Wimmer, Weitian Tong

Georgia Southern University

Research output: Contribution to book or proceeding › Conference article › peer-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.

Original language	English
Title of host publication	2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021
Editors	Satyajit Chakrabarti, Rajashree Paul
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	371-377
Number of pages	7
ISBN (Electronic)	9781665400664
DOIs	https://doi.org/10.1109/IEMCON53756.2021.9623132 https://doi.org/10.1109/IEMCON53756.2021.9623132
State	Published - Dec 6 2021
Event	IEEE Annual Information Technology, Electronics and Mobile Communication Conference - Vancouver, Canada Duration: Oct 27 2021 → Oct 30 2021 Conference number: 12 https://ieeexplore.ieee.org/servlet/opac?punumber=9623060

Publication series

Name	2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

Conference

Conference	IEEE Annual Information Technology, Electronics and Mobile Communication Conference
Abbreviated title	IEEE IEMCON
Country/Territory	Canada
City	Vancouver
Period	10/27/21 → 10/30/21
Internet address	https://ieeexplore.ieee.org/servlet/opac?punumber=9623060

Scopus Subject Areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Information Systems
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality

Disciplines

Computer Sciences

Keywords

Bitcoin
Cryptocurrency
Deep Learning
GPU
LSTM
RNN

Access to Document

10.1109/IEMCON53756.2021.9623132
10.1109/IEMCON53756.2021.9623132License: Unspecified

https://doi.org/10.1109/IEMCON53756.2021.9623132

Cite this

Trawinski, A., Wimmer, H., & Tong, W. (2021). Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures. In S. Chakrabarti, & R. Paul (Eds.), 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021 (pp. 371-377). (2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEMCON53756.2021.9623132, https://doi.org/10.1109/IEMCON53756.2021.9623132

Trawinski, Alex ; Wimmer, Hayden ; Tong, Weitian. / Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021. editor / Satyajit Chakrabarti ; Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 371-377 (2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021).

@inproceedings{462026d61a2144ba94ee8186bef27a7c,

title = "Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures",

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.",

keywords = "Bitcoin, Cryptocurrency, Deep Learning, GPU, LSTM, RNN",

author = "Alex Trawinski and Hayden Wimmer and Weitian Tong",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE Annual Information Technology, Electronics and Mobile Communication Conference ; Conference date: 27-10-2021 Through 30-10-2021",

year = "2021",

month = dec,

day = "6",

doi = "10.1109/IEMCON53756.2021.9623132",

language = "English",

series = "2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "371--377",

editor = "Satyajit Chakrabarti and Rajashree Paul",

booktitle = "2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021",

address = "United States",

url = "https://ieeexplore.ieee.org/servlet/opac?punumber=9623060",

}

Trawinski, A, Wimmer, H & Tong, W 2021, Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures. in S Chakrabarti & R Paul (eds), 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021, Institute of Electrical and Electronics Engineers Inc., pp. 371-377, IEEE Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, British Columbia, Canada, 10/27/21. https://doi.org/10.1109/IEMCON53756.2021.9623132, https://doi.org/10.1109/IEMCON53756.2021.9623132

Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures. / Trawinski, Alex; Wimmer, Hayden ; Tong, Weitian.
2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021. ed. / Satyajit Chakrabarti; Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2021. p. 371-377 (2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures

AU - Trawinski, Alex

AU - Wimmer, Hayden

AU - Tong, Weitian

N1 - Conference code: 12

PY - 2021/12/6

Y1 - 2021/12/6

N2 - 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.

AB - 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.

KW - Bitcoin

KW - Cryptocurrency

KW - Deep Learning

KW - GPU

KW - LSTM

KW - RNN

UR - https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/179

UR - https://doi.org/10.1109/IEMCON53756.2021.9623132

U2 - 10.1109/IEMCON53756.2021.9623132

DO - 10.1109/IEMCON53756.2021.9623132

M3 - Conference article

AN - SCOPUS:85123583861

T3 - 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

SP - 371

EP - 377

BT - 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

A2 - Chakrabarti, Satyajit

A2 - Paul, Rajashree

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Annual Information Technology, Electronics and Mobile Communication Conference

Y2 - 27 October 2021 through 30 October 2021

ER -

Trawinski A, Wimmer H , Tong W. Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures. In Chakrabarti S, Paul R, editors, 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 371-377. (2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021). doi: 10.1109/IEMCON53756.2021.9623132, 10.1109/IEMCON53756.2021.9623132

Deep Learning on Cryptocurrency - Challenges Processing GPU and CPU Architectures

Abstract

Publication series

Conference

Scopus Subject Areas

Disciplines

Keywords

Access to Document

Other files and links

Fingerprint

Cite this