Flexible Hardware Architecture of Hierarchical K-Means Clustering for Large Cluster Number

Abstract

K-Means is an important clustering algorithm that is widely applied to different applications, including color clustering and image segmentation. To handle large cluster numbers in embedded systems, a hardware architecture of hierarchical K-Means (HK-Means) is proposed to support a maximum cluster number of 1024. It adopts 10 processing elements for the Euclidean distance computations and the level-order binary-tree traversal. Besides, a hierarchical memory structure is integrated to offer a maximum bandwidth of 1280 bit/cycle to processing elements. The experiments show that applications such as video segmentation and color quantization can be implemented based on the proposed HK-Means hardware. Moreover, the gate count of the hardware is 414 K, and the maximum frequency achieves 333 MHz. It supports the highest cluster number and has the most flexible specifications among our works and related works.