Coordinate Rotation-Based Low Complexity K-Means Clustering Architecture
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This document proposes a low-complexity K-means clustering architecture using a Coordinate Rotation Digital Computer-based engine to efficiently compute Euclidean distances between data points and centroids. The architecture was synthesized using a 130nm process and estimated to have a core area of 0.36 mm^2 and power consumption of 9.21 mW at 100 MHz, making it suitable for real-time low-power applications like mobile health monitoring. The proposed design was analyzed using ModelSim and Xilinx ISE software.
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Coordinate Rotation-Based Low Complexity K-Means Clustering Architecture
- 1. Coordinate Rotation-Based Low Complexity K-Means Clustering Architecture 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 mm2 and 9.21 mW at 100 MHz, respectively, making the design applicable for low-power real-time operations within a sensor node. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.
- 2. SOFTWARE IMPLEMENTATION: Modelsim Xilinx ISE