Fast knn classification based on softcore cpu and reconfigurable hardware

Hui Ya Li, Yao Jung Yeh, Wen Jyi Hwang

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)


This paper presents a novel architecture for k-nearest neighbor (kNN) classification using field programmable gate array (FPGA). In the architecture, the first k closest vectors in the design set of a kNN classifier for each input vector are first identified by perfomung the partial distance search (PDS) in the wavelet domain. To implement the PDS in hardware, subspace search, bitplane reduction, multiple-coefficient accumulation and multiple-module computation techniques are employed for the effective reduction of the area complexity and computation latency. The proposed implementation has been embedded in a softcore CPU for physical performance measurement. Experimental results show that the implementation provides acosteffective solution to the FPGA realization of kNN classification systems where both high throughput and low area cost are desired.

頁(從 - 到)431-446
期刊Intelligent Automation and Soft Computing
出版狀態已發佈 - 2011 一月

ASJC Scopus subject areas

  • 軟體
  • 理論電腦科學
  • 計算機理論與數學
  • 人工智慧


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