Abstract
The artificial neural network (NN) is widely use in pattern recognition related area such as classification. After all this time, the computational process of NN is done using central processing unit (CPU). In recent years, the introduction of graphics processing unit (GPU) has opened another way to perform calculations with the advantage to speed up the calculation. In this paper, the computational process of multilayer perceptron neural network be tested on GPU using classification datasets. The performance of NN model with different number of input, hidden and output neurons are explored and compared based on the computational between GPU and CPU. The experimental result shows that the computational on GPU is much faster than CPU.
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Acknowledgment
This study is supported by the Fundamental Research Grant Scheme (FRGS vots: 4F738 & 4F550) that sponsored by Ministry of Higher Education (MOHE). Authors would like to thank Research Management Centre (RMC) Universiti Teknologi Malaysia, for the research activities and Soft Computing Research Group (SCRG) for the support and motivation in making this study a success.
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Anuar, S., Sallehuddin, R., Selamat, A. (2016). Implementation of Artificial Neural Network on Graphics Processing Unit for Classification Problems. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_29
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DOI: https://doi.org/10.1007/978-3-319-45246-3_29
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