Highly Parameterized K-means Clustering on FPGAs: Comparative Results with GPPs and GPUs

Abstract

K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amount of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors or GPPs to process large datasets may take a long time, therefore many acceleration methods have been proposed in the literature to speed-up the processing of such large datasets. In this work, we propose a parameterized Field Programmable Gate Array (FPGA) implementation of the K-means algorithm and compare it with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and with GPPs. The proposed FPGA implementation has shown higher performance in terms of speed-up over previous FPGA GPU and GPP implementations, and is more energy efficient.