Accelerating nearest neighbor partitioning neural network classifier based on CUDA. (February 2018)

• Record Type: Journal Article
• Authors: Wang, Lin; Zhu, Xuehui; Yang, Bo; Guo, Jifeng; Liu, Shuangrong; Li, Meihui; Zhu, Jian; Abraham, Ajith
• Is Part Of: Engineering applications of artificial intelligence. Volume 68(2017:Aug.)
• Page Start: 53
• Page End: 62
• Publication Date: 2018-02
• Subjects: Parallel nearest neighbor partitioning -- Neural network classifier -- Compute unified device architecture -- Graphics processing units; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.10.023
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The nearest neighbor partitioning (NNP) method is a high performance approach which is used for improving traditional neural network classifiers. However, the construction process of NNP model is very time-consuming, particularly for large data sets, thus limiting its range of application. In this study, a parallel NNP method is proposed to accelerate NNP based on Compute Unified Device Architecture(CUDA). In this method, blocks and threads are used to evaluate potential neural networks and to perform parallel subtasks, respectively. Experimental results manifest that the proposed parallel method improves performance of NNP neural network classifier. Furthermore, the application of parallel NNP in performance evaluation of cement microstructure indicates that the proposed approach has favorable performance.