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

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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.

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IITH Creators:
IITH CreatorsORCiD
Acharyya, Amit
Item Type: Article
Uncontrolled Keywords: Coordinate Rotation Digital Computer (CORDIC); hardware design; K-means; low complex architecture; signal processing
Subjects: Computer science > Special computer methods
Others > Engineering technology
Electrical Engineering > Electrical and Electronic
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 23 Jun 2017 04:51
Last Modified: 29 Aug 2017 10:48
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