Coordinate Rotation-Based Low Complexity $K$ -Means Clustering Architecture

Abstract

Abstract-In this paper, we propose a low-complexity architectural implementation of the K-Means based clustering algorithm used widely in mobile health monitoring applications for unsupervised and supervised learning. The iterative nature of the algorithm, computing the distance of each data point from a respective centroid for a successful cluster formation until convergence presents a significant challenge to map it onto a lowpower architecture. This has been addressed by the use of a 2-D Coordinate Rotation Digital Computer (CORDIC) based lowcomplexity engine for computing the n-dimensional Euclidean distance involved during clustering. The proposed clustering engine was synthesized using the TSMC 130 nm technology library and a place and route was performed following which the core area and power were estimated as 0.36mm 2 and 9.21mW @ 100 Mhz respectively making the design applicable for low-power real-time operations within a sensor node.Index Terms-K-Means, CORDIC, signal processing, hardware design, low complex architecture.