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

作     者：温程璐 潘伟 陈晓熹 祝青园 

作者机构：Department of Cognitive ScienceXiamen University Fujian Provincial Key Laboratory of Brain-Like Intelligent Systems Department of Computer ScienceXiamen University Department of Mechanical and Electrical EngineeringXiamen University 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2012年第29卷第1期

页      面：5-8页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China (No. 60975084) Natural Science Foundation of Fujian Province,China (No.2011J05159) 

主　　题：graphic processing unit(GPU) compute unified device architecture (CUDA) neural network species recognition 

摘      要：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 species 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.