Adaptive resonance theory fuzzy networks parallel computation using CUDA
Published in International Work-Conference on Artificial Neural Networks, 2009
Recommended citation: Martínez-Zarzuela, M., Diaz-Pernas, F., Tejero-de-Pablos, A., Anton-Rodríguez, M., Diez-Higuera, J., Boto-Giralda, D., & Gonzalez-Ortega, D. (2009, June). Adaptative resonance theory fuzzy networks parallel computation using CUDA. In International Work-Conference on Artificial Neural Networks (pp. 149-156)
Programming of Graphics Processing Units (GPUs) has evolved in a way they can be used to address and speed-up computation of algorithms exemplified by data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training under specific conditions.
Bibtex:
@inproceedings{martinez2009adaptative,
title={Adaptative resonance theory fuzzy networks parallel computation using CUDA},
author={Mart{\'\i}nez-Zarzuela, Mario and Pernas, FJ and Pablos, A de and Rodr{\'\i}guez, M Ant{\'o}n and Higuera, JF and Giralda, D Boto and Ortega, D Gonz{\'a}lez},
booktitle={International Work-Conference on Artificial Neural Networks},
pages={149--156},
year={2009},
organization={Springer}
}