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

Adapa, B and Biswas, D and Bhardwaj, S and Raghuraman, S and Acharyya, Amit and Maharatna, K (2017) Coordinate Rotation-Based Low Complexity $K$ -Means Clustering Architecture. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25 (4). pp. 1568-1572. ISSN 1063-8210

Full text not available from this repository. (Request a copy)

Abstract

In this brief, 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 low-power architecture. This has been addressed by the use of a 2-D Coordinate Rotation Digital Computer-based low-complexity 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 0.36 mm(2) and 9.21 mW at 100 MHz, respectively, making the design applicable for low-power real-time operations within a sensor node.

[error in script]

IITH Creators: