Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware
Mike Estlick(Northeastern University), Miriam Leeser(Northeastern University), James Theiler(Los Alamos National Laboratory), J. Szymański(Los Alamos National Laboratory)
Cited by 126
Abstract
In mapping the k-means algorithm to FPGA hardware, we examined algorithm level transforms that dramatically increased the achievable parallelism. We apply the k-means algorithm to multi-spectral and hyper-spectral images, which have tens to hundreds of channels per pixel of data. K-means is an iterative algorithm that assigns assigns to each pixel a label indicating which of K clusters the pixel belongs to.
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