GPU implementation of the multiple back-propagation algorithm

Authors:
Noel Lopes;Bernardete Ribeiro
Affiliations:
Center for Informatics and Systems of University of Coimbra, Portugal and UDI, Research Unit, Polytechnic Institute of Guarda, Portugal;Center for Informatics and Systems of University of Coimbra, Portugal
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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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.