Abstract
Graphics Processing Units (GPUs) can provide remarkable performance gains when compared to CPUs for computationally-intensive applications. Thus they are much attractive to be used as dedicated hardware in many fields such as in machine learning. In particular, the implementation of neural networks (NNs) in GPUs can decrease enormously the long training times during the learning process. In this paper, we describe a parallel implementation of the Multiple Back-Propagation (MBP) algorithm and present the results obtained when running the algorithm on two well-known benchmarks. We show that for both classification and regression problems our implementation reduces the computational cost when compared with the standalone CPU version.
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Lopes, N., Ribeiro, B. (2009). GPU Implementation of the Multiple Back-Propagation Algorithm. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_55
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DOI: https://doi.org/10.1007/978-3-642-04394-9_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
Online ISBN: 978-3-642-04394-9
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