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Parallel backpropagation neural network training for face recognition

In this paper, we describe implementation of ANN training process using backpropagation learning algorithm for exploiting the high performance SIMD architecture of GPU using CUDA. We also compare sequential and parallel algorithm execution times and conducted speedup analysis for both the methods. T...

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Main Authors: Omarov B., Suliman A., Tsoy A.
Other Authors: 57202103462
Format: Article
Published: Pushpa Publishing House 2023
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spelling my.uniten.dspace-226062023-05-29T14:11:18Z Parallel backpropagation neural network training for face recognition Omarov B. Suliman A. Tsoy A. 57202103462 25825739000 57192438194 In this paper, we describe implementation of ANN training process using backpropagation learning algorithm for exploiting the high performance SIMD architecture of GPU using CUDA. We also compare sequential and parallel algorithm execution times and conducted speedup analysis for both the methods. The simulation results demonstrate a significant decrease on executing times and greater speedup than serial implementation of training and learning processes. All due to the parallel algorithm and use of the GPU, the training time for huge set of images get reduced significantly increasing the accuracy rate of face recognition. � 2016 Pushpa Publishing House, Allahabad, India. Final 2023-05-29T06:11:18Z 2023-05-29T06:11:18Z 2016 Article 10.17654/EC016040801 2-s2.0-85006333151 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006333151&doi=10.17654%2fEC016040801&partnerID=40&md5=0e50d96869b149ea664bd22663aff2ee https://irepository.uniten.edu.my/handle/123456789/22606 16 4 801 808 Pushpa Publishing House Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description In this paper, we describe implementation of ANN training process using backpropagation learning algorithm for exploiting the high performance SIMD architecture of GPU using CUDA. We also compare sequential and parallel algorithm execution times and conducted speedup analysis for both the methods. The simulation results demonstrate a significant decrease on executing times and greater speedup than serial implementation of training and learning processes. All due to the parallel algorithm and use of the GPU, the training time for huge set of images get reduced significantly increasing the accuracy rate of face recognition. � 2016 Pushpa Publishing House, Allahabad, India.
author2 57202103462
author_facet 57202103462
Omarov B.
Suliman A.
Tsoy A.
format Article
author Omarov B.
Suliman A.
Tsoy A.
spellingShingle Omarov B.
Suliman A.
Tsoy A.
Parallel backpropagation neural network training for face recognition
author_sort Omarov B.
title Parallel backpropagation neural network training for face recognition
title_short Parallel backpropagation neural network training for face recognition
title_full Parallel backpropagation neural network training for face recognition
title_fullStr Parallel backpropagation neural network training for face recognition
title_full_unstemmed Parallel backpropagation neural network training for face recognition
title_sort parallel backpropagation neural network training for face recognition
publisher Pushpa Publishing House
publishDate 2023
_version_ 1806428496155639808
score 13.214268

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