Graphic Processing Unit-Accelerated Neural Network Model for Biological Species Recognition

• Published in: 东华大学学报（英文版） Vol. 29; no. 1; pp. 5 - 8
• Main Author: 温程璐 潘伟 陈晓熹 祝青园
• Format: Journal Article
• Language: English
• Published: Fujian Provincial Key Laboratory of Brain-Like Intelligent Systems,Xiamen 361005,China%Department of Computer Science,Xiamen University,Xiamen 361005,China%Department of Mechanical and Electrical Engineering,Xiamen University,Xiamen 361005,China 2012; Department of Cognitive Science,Xiamen University,Xiamen 361005,China
• Subjects: graphic processing unit(GPU); compute unified device architecture (CUDA); species recognition; neural network
• ISSN: 1672-5220

A graphic processing unit （GPU）-accelerated biological species recognition method using partially connected neural evolutionary network model is introduced in this paper. The partial connected neural evolutionary network adopted in the paper can overcome the disadvantage of traditional neural network with small inputs. The whole image is considered as the input of the neural network, so the maximal features can be kept for recognition. To speed up the recognition process of the neural network, a fast implementation of the partially connected neural network was conducted on NVIDIA Tesla C1060 using the NVIDIA compute unified device architecture （CUDA） framework. Image sets of eight biological spedes were obtained to test the GPU implementation and counterpart serial CPU implementation, and experiment results showed GPU implementation works effectively on both recognition rate and speed, and gained 343 speedup over its counterpart CPU implementation. Comparing to feature-based recognition method on the same recognition task, the method also achieved an acceptable correct rate of 84.6 % when testing on eight biological species.