Optimization of a Principal Component Analysis Implementation on Field-Programmable Gate Arrays (FPGA) for Analysis of Spectral Images

For the acceptance of spectral measurement technology for quality assurance and inspection in the industrial sector, the acquisition and processing of spectral images must be adapted to the production cycle. When processing spectral images, variations of the Principal Component Analysis (PCA) are often used as preprocessing steps, for example for segmentation, spectral decomposition or data compression. To speed up this time-consuming algorithm, hardware and software cores were implemented on a system-on-a-programmable-chip (SoPC). This paper deals with the optimization of this implementation to minimize calculation times. Special attention is paid to the cores used to calculate covariances and data derivation. The restructuring of the hardware IP (Intellectual property) cores and fundamental design decisions are discussed. The optimization was implemented and evaluated on a 12-channel spectral camera.