Multiprocessor architecture for real-time applications using mean shift clustering

Abstract

Data mining and machine learning algorithms deal with large amount of data, which with the invention of cost-efficient devices has increased by massive amounts. Many algorithms of these domains are not part of real-time systems because of their computational complexity and large data on which they need to work. A lot of algorithms are being implemented on parallel processing systems like GPUs and FPGAs to achieve the desired speed. The purpose of this article is to provide parallel processing model of mean shift clustering algorithm, targeted to run on FPGA. The general model consists of multiple homogeneous processing entities (PEs) connected through a bus. These PEs work in collaborative working environment with each PE working independently and also communicating with its peers according to the requirements of algorithms. The proposed architecture is implemented on FPGA for one-dimensional data. The algorithm is tested on 99 images from segmentation evaluation database for different number of PEs and different number of fractional bits used to represent mean. The simplicity of algorithm resulted in utilizing only 10.31% of total device slice registers and 33% of total slice LUTs of Spartan 6 FPGA. The processing requirements for the proposed algorithm show that it can be used in real-time systems.