An FPGA implementation of parallel ICA for dimensionality reduction in hyperspectral images

Independent component analysis (ICA) is a technique that extracts independent source signals by searching for a linear or nonlinear transformation which minimizes the statistical dependence between components. ICA has been used in a variety of signal processing applications including dimensionality reduction in hyperspectral image (HSI) analysis. Due to the computation complexities and convergence rates, ICA is very time-consuming for high volume or dimension data set like hyperspectral images. Hardware implementation provides not only an optimal parallelism environment, but also a potential faster and real-time solution. This work synthesizes a parallel ICA (pICA) algorithm on field programmable gate array (FPGA). In the proposed implementation method, the pICA is partitioned into three temporally independent functional modules, and each of which is synthesized individually with several ICA-related reconfigurable components (RCs) that are developed for reuse and retargeting purpose. All modules are then integrated into a design and development environment for performing many subtasks such as FPGA synthesis, optimization, placement and routing. In a case study, we synthesize the pICA algorithm for hyperspectral image dimensionality reduction on the pilchard reconfigurahle computing platform embedded with Xilinx: VIRTEX V1000E. The FPGA executes at the maximum frequency of 20.161 MHz, and the pilchard board transfers data directly with CPU on the 64-bit memory bus at the maximum frequency of 133MHz. The performance comparisons between the proposed and another two ICA-related FPGA implementations show that the proposed FPGA implementation of pICA has potential in performing complicated algorithms on large volume data sets.