Coordinate Rotation-Based Low Complexity $K$ -Means Clustering Architecture
Bhagyaraja Adapa, Dwaipayan Biswas, Swati Bhardwaj, Shashank Raghuraman, Amit Acharyya, Koushik Maharatna · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2017
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 mm2and 9.21 mW at 100 MHz, respectively, making the design applicable for low-power real-time operations within a sensor node.