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Fuzzy ART Neural Network Parallel Computing on the GPU

Mario Martinez-Zarzuela, Diaz, Diez, Miriam Rodriguez
Higher School of Telecommunications Engineering, University of Valladolid, Spain
Computational and Ambient Intelligence (2007), pp. 463-470.

@conference{martinez2007fuzzy,

   title={Fuzzy ART neural network parallel computing on the GPU},

   author={Mart{‘i}nez-Zarzuela, M. and Pernas, F.J.D. and Higuera, J.F.D. and Rodr{‘i}guez, M.A.},

   booktitle={Proceedings of the 9th international work conference on Artificial neural networks},

   pages={463–470},

   year={2007},

   organization={Springer-Verlag}

}

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Graphics Processing Units (GPUs) have evolved into powerful programmable processors, faster than Central Processing Units (CPUs) regarding the execution of parallel algorithms. In this paper, an implementation of a Fuzzy ART Neural Network on the GPU is presented. Experimental results show training process is slower on the GPU than on a dual-core Pentium 4 at 3.2 GHz. Once the Neural Network has been trained, the proposed design manages to accelerate Fuzzy ART testing process up to 33 times on a GeForce 7800GT graphics card.
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