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.

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