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An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks Cover

An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks

Journal of Artificial Intelligence and Soft Computing Research
Volume 11 (2021): Issue 1 (January 2021)
By: Julia El Zini,  Yara Rizk and  Mariette Awad  
Open Access
|Dec 2020
Authors

Authors

Julia El Zini

jwe04@aub.edu.lb

Department of Electrical and Computer Engineering, American University of Beirut

Yara Rizk

yar01@aub.edu.lb

Department of Electrical and Computer Engineering, American University of Beirut

Mariette Awad

mariette.awad@aub.edu.lb

Department of Electrical and Computer Engineering, American University of Beirut
DOI: https://doi.org/10.2478/jaiscr-2021-0003
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Language: English
Page range: 33 - 50
Submitted on: May 7, 2020
Accepted on: Sep 14, 2020
Published on: Dec 3, 2020
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
GPU implementation,
parallelization,
Recurrent Neural Network (RNN),
Long-short Term Memory (LSTM),
Gated Recurrent Unit (GRU),
Extreme Learning Machines (ELM),
non-iterative training
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2020 Julia El Zini, Yara Rizk, Mariette Awad, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 11 (2021): Issue 1 (January 2021)
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