Bandwidth Adaptive Hardware Architecture of K-Means Clustering for Video Analysis
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.
k-means clustering methods hardware design parallel architectures pattern recognition