CuParcone一种高性能可进化神经网络模型

Xiaoxi Chen, Lin Gao, H. de Garis
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引用次数: 2

摘要

在CUDA上实现了一种通过遗传算法进化递归神经网络的算法,从而形成了一个名为CuParcone(基于CUDA的部分连接神经进化)的系统。在Nvidia Tesla“GPU超级计算机”上运行的CuParcone在人脸性别识别方面的性能比在最先进的商用单处理器服务器上运行的同类Parcone算法提高了323倍。在从Parcone到CuParcone的移动中,该任务的准确性不会降低,并且与其他已发表的算法的结果相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CuParcone A High-Performance Evolvable Neural Network Model
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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