Title :
CuParcone A High-Performance Evolvable Neural Network Model
Author :
Chen, Xiaoxi ; Gao, Lin ; De Garis, Hugo
Author_Institution :
Xiamen Univ. Sci. & Technol., Xiamen, China
Abstract :
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.
Keywords :
computer graphic equipment; coprocessors; genetic algorithms; recurrent neural nets; CUDA; CuParcone; GPU supercomputer; Parcone; evolvable neural network model; genetic algorithm; recurrent neural network; Application software; Clustering algorithms; Computer architecture; Computer networks; Concurrent computing; Face recognition; Neural networks; Neurons; Parallel processing; Recurrent neural networks; CUDA; CuParcone; GPU; Gender Recognition; Genetic Algorithms; Neural Networks; Parcone;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
DOI :
10.1109/ICICTA.2010.479