Masters Theses
Date of Award
5-2004
Degree Type
Thesis
Degree Name
Master of Science
Major
Electrical Engineering
Major Professor
Gregory Peterson
Committee Members
Donald W. Bouldin, Hairong Qi, Chandra Tan
Abstract
Processing power of pattern classification algorithms on conventional platforms has not been able to keep up with exponentially growing datasets. However, algorithms such as k-means clustering include significant potential parallelism that could be exploited to enhance processing speed on conventional platforms. A better and effective solution to speed-up the algorithm performance is the use of a hardware assist since parallel kernels can be partitioned and concurrently run on hardware as opposed to the sequential software flow. A parameterized hardware implementation of k-means clustering is presented as a proof of concept on the Pilchard Reconfigurable computing system. The hardware implementation is shown to have speedups of about 500 over conventional implementations on a general-purpose processor. A scalability analysis is done to provide a future direction to take the current implementation of 3 classes and scale it to over N classes.
Recommended Citation
Bhaskaran, Venkatesh, "Parameterized Implementation of K-means Clustering on Reconfigurable Systems. " Master's Thesis, University of Tennessee, 2004.
https://trace.tennessee.edu/utk_gradthes/4648