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CuParcone A High-Performance Evolvable Neural Network Model

Xiaoxi Chen, Lin Gao, Hugo de Garis
Xiamen Univ. Sci. & Technol., Xiamen, China
International Conference on Intelligent Computation Technology and Automation (ICICTA), 2010

@inproceedings{chen2010cuparcone,

   title={CuParcone A High-Performance Evolvable Neural Network Model},

   author={Chen, X. and Gao, L. and de Garis, H.},

   booktitle={Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation-Volume 01},

   pages={1070–1074},

   year={2010},

   organization={IEEE Computer Society}

}

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An algorithm for evolving recurrent neural network via the genetic algorithm was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary). Run on a Nvidia Tesla "GPU supercomputer," CuParcone achieves a performance increase of 323 times in face gender recognition compared to the comparable Parcone algorithm on a state-of-the-art, commodity single-processor server. The accuracy on this task does not decrease in moving from Parcone to CuParcone, and is comparable to the published results of other algorithms.
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