Bandwidth adaptive hardware architecture of K-means clustering for video analysis
Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Journal Volume
18
Journal Issue
6
Pages
957-966
Date Issued
2010
Author(s)
Chen, T.-W.
Abstract
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 can be used in applications such as image segmentation, and it has the maximum clock speed 400-MHz and 440-K gate count with TSMC 90-nm technology. Moreover, the throughput of the proposed hardware reaches 16 dimension/cycle, and it can deal with feature vectors with different dimensions using five parallel modes to utilize the input bandwidth efficiently. © 2009 IEEE.
SDGs
Type
journal article