BANDWIDTH ADAPTIVE HARDWARE ARCHITECTURE OF K-MEANS CLUSTERING FOR INTELLIGENT VIDEO PROCESSING

被引：2

作者：

Chen, Tse-Wei ^{[1
]}

Chien, Shao-Yi ^{[1
]}

机构：

[1] Natl Taiwan Univ, Grad Inst Elect Engn, Media IC & Syst Lab, Dept Elect Engn, BL-421,1,Sec 4,Roosevelt Rd, Taipei 106, Taiwan

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

K-Means; clustering methods; pattern recognition; parallel architectures; hardware design;

D O I：

10.1109/ICASSP.2009.4959648

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

K-Means is a clustering algorithm that is widely applied in many fields, including pattern classification and multimedia analysis. Due to real-time requirements and computational-cost constraints in embedded systems, it is necessary to accelerate K-Means algorithm by hardware implementations in SoC environments, where the bandwidth of the system bus is strictly limited. In this paper, a bandwidth adaptive hardware architecture of K-Means clustering is proposed. Experiments show that the proposed hardware has the maximum clock speed 400MHz with TSMC 90nm technology, and it can deal with feature vectors with different dimensions using five parallel modes to utilize the input bandwidth efficiently.

引用

页码：573 / +

页数：2

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