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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Parallel Backpropagation Neural Network Training Techniques using Graphics Processing Unit

Author 1: Muhammad Arslan Amin Author 2: Muhammad Kashif Hanif Author 3: Muhammad Umer Sarwar Author 4: Abdur Rehman Author 5: Fiaz Waheed Author 6: Haseeb Rehman
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 10, No. 2 · Published 2019 · Cited by 5

DOI: https://doi.org/10.14569/IJACSA.2019.0100270

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Abstract

Training of artificial neural network using back-propagation is a computational expensive process in machine learning. Parallelization of neural networks using Graphics Pro-cessing Unit (GPU) can help to reduce the time to perform computations. GPU uses a Single Instruction Multiple Data (SIMD) architecture to perform high speed computing. The use of GPU shows remarkable performance gain when compared to CPU. This work discusses different parallel techniques for the backpropagation algorithm using GPU. Most of the techniques perform comparative analysis between CPU and GPU.

Keywords

Artificial neural network backpropagation SIMD CPU GPU machine learning

How to Cite this Article

Amin, M. A., Hanif, M. K., Sarwar, M. U., Rehman, A., Waheed, F., & Rehman, H. (2019). Parallel Backpropagation Neural Network Training Techniques using Graphics Processing Unit. International Journal of Advanced Computer Science and Applications, 10(2). https://doi.org/10.14569/IJACSA.2019.0100270

Amin, Muhammad Arslan, et al.. "Parallel Backpropagation Neural Network Training Techniques using Graphics Processing Unit." International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, 2019, https://doi.org/10.14569/IJACSA.2019.0100270.

@article{Amin2019,
  title     = {Parallel Backpropagation Neural Network Training Techniques using Graphics Processing Unit},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {2},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Arslan Amin and Muhammad Kashif Hanif and Muhammad Umer Sarwar and Abdur Rehman and Fiaz Waheed and Haseeb Rehman},
  doi       = {10.14569/IJACSA.2019.0100270},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100270}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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