A Reconfigurable FPGA System for Parallel Independent Component Analysis
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A Reconfigurable FPGA System for Parallel Independent Component Analysis Hongtao Du and Hairong Qi Electrical and Computer Engineering Department, The University of Tennessee, Knoxville, TN 37996-2100, USA Received 13 December 2005; Revised 12 September 2006; Accepted 15 September 2006 Recommended for Publication by Miriam Leeser A run-time reconfigurable field programmable gate array (FPGA) system is presented for the implementation of the parallel independent component analysis (ICA) algorithm. In this work, we investigate design challenges caused by the capacity constraints of single FPGA. Using the reconfigurability of FPGA, we show how to manipulate the FPGA-based system and execute processes for the parallel ICA (pICA) algorithm. During the implementation procedure, pICA is first partitioned into three temporally independent function blocks, each of which is synthesized by using several ICA-related reconfigurable components (RCs) that are developed for reuse and retargeting purposes. All blocks are then integrated into a design and development environment for performing tasks such as FPGA optimization, placement, and routing. With partitioning and reconfiguration, the proposed reconfigurable FPGA system overcomes the capacity constraints for the pICA implementation on embedded systems. We demonstrate the effectiveness of this implementation on real images with large throughput for dimensionality reduction in hyperspectral image (HSI) analysis. Copyright © 2006 H. Du and H. Qi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
In recent years, independent component analysis (ICA) has played an important role in a variety of signal and image processing applications such as blind source separation (BSS) [1], recognition [2], and hyperspectral image (HSI) analysis [3]. In these applications, the observed signals are generally the linear combinations of the source signals. For example, in the cocktail party problem, the acoustic signal captured from any microphone is a mixture of individual speakers (source signal) speaking at the same time; in the case of hyperspectral image analysis, since each pixel in the hyperspectral image could cover hundreds of square feet area that contains many different materials, unmixing the hyperspectral image (the observed signal or mixed signal) to the pure materials (source signals) is a critical step before any other processing algorithms can be practically applied. ICA is a very effective technique for unsupervised source signal estimations, given only the observations of mixed signals. It searches for a linear or nonlinear transformation to minimize the higher-order statistical dependence between the source signals [4, 5]. Although powerful, ICA is very time consuming in software implementations due to the
computation complexities and the slow convergence rate, especially for high-volume or dimensiona
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