Bandwidth adaptive hardware architecture of K-Means clustering for intelligent video processing
Tse‐Wei Chen, Shao‐Yi Chien · 2009
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 400 MHz with TSMC 90 nm technology, and it can deal with feature vectors with different dimensions using five parallel modes to utilize the input bandwidth efficiently.