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Coordinate rotation based low complexity K-means clustering architecture

Coordinate rotation based low complexity K-means clustering architecture
Coordinate rotation based low complexity K-means clustering architecture
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.
k-means algorithm, hardware design, CORDIC, Signal Processing, Low-power architecture
1063-8210
1568-1572
Adapa, Bhagyaraja
2ac6f05f-9d36-40ad-abc7-e4ddee737922
Biswas, Dwaipayan
314a210f-c293-4d18-8b07-ddaaf57a1707
Bhardwaj, Swati
f3d77c2d-151e-4262-879a-8a0085ed84ac
Raghuraman, Shashank
019ef9f9-9252-4284-8245-61a3f3630d3c
Acharyya, Amit
30ea0463-5660-437f-a86b-d98d3914ecaa
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Adapa, Bhagyaraja
2ac6f05f-9d36-40ad-abc7-e4ddee737922
Biswas, Dwaipayan
314a210f-c293-4d18-8b07-ddaaf57a1707
Bhardwaj, Swati
f3d77c2d-151e-4262-879a-8a0085ed84ac
Raghuraman, Shashank
019ef9f9-9252-4284-8245-61a3f3630d3c
Acharyya, Amit
30ea0463-5660-437f-a86b-d98d3914ecaa
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Adapa, Bhagyaraja, Biswas, Dwaipayan, Bhardwaj, Swati, Raghuraman, Shashank, Acharyya, Amit and Maharatna, Koushik (2017) Coordinate rotation based low complexity K-means clustering architecture IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25, (4), pp. 1568-1572. (doi:10.1109/TVLSI.2016.2633543).

Record type: Article

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.

PDF Coordinate Rotation Based Low Complexity - Accepted Manuscript
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More information

Accepted/In Press date: 2 November 2016
e-pub ahead of print date: 10 January 2017
Published date: April 2017
Keywords: k-means algorithm, hardware design, CORDIC, Signal Processing, Low-power architecture
Organisations: Electronics & Computer Science, Electronic & Software Systems

Identifiers

Local EPrints ID: 411121
URI: http://eprints.soton.ac.uk/id/eprint/411121
ISSN: 1063-8210
PURE UUID: f1e4d82e-88a9-4e15-884a-7c9bd471d06b

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Date deposited: 14 Jun 2017 16:31
Last modified: 24 Jul 2017 16:33

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Contributors

Author: Bhagyaraja Adapa
Author: Dwaipayan Biswas
Author: Swati Bhardwaj
Author: Shashank Raghuraman
Author: Amit Acharyya

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