CuParcone - a high-performance evolvable neural network model

摘要：

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.

关键词： CuParcone Parcone CUDA GPU Genetic Algorithms Neural Networks Gender Recognition

作者: Xiaoxi Chen Lin Gao Hugo de GARIS

作者单位: Xiamen University Science & Technology, Xiamen, Fujian,361005, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 1070-1074

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）