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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2023
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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 |
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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 |
13.250246 |