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Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing


This paper presents a study of the adaptation of a Non-Linear Iterative Partial Least Squares (NIPALS) algorithm applied to Hyperspectral Imaging to a Massively Parallel Processor Array manycore architecture, which assembles 256 cores distributed over 16 clusters. This work aims at optimizing the internal communications of the platform to achieve real-time processing of large data volumes with limited computational resources and memory bandwidth. As hyperspectral images are composed of extensive volumes of spectral information, real-time requirements, which are upper-bounded by the image capture rate of the hyperspectral sensor, are a challenging objective. To address this issue, the image size is usually reduced prior to the processing phase, which is itself a computationally intensive task. Consequently, this paper proposes an analysis of the intrinsic parallelism and the data dependency within the NIPALS algorithm and its subsequent implementation on a manycore architecture. Furthermore, this implementation has been validated against three hyperspectral images extracted from both remote sensing and medical datasets. As a result, an average speedup of 17× has been achieved when compared to the sequential version. Finally, this approach has been compared with other state-of-the-art implementations, outperforming them in terms of performance.

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This research has been funded by the EU FET HELICoiD (HypErspectraL Imaging Cancer Detection) project (FP7-ICT-2013.9.2 (FET Open) 618080).

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Lazcano, R., Madroñal, D., Fabelo, H. et al. Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing. J Sign Process Syst 91, 759–771 (2019).

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  • Hyperspectral imaging
  • Massively parallel processing
  • Real-time processing
  • Parallel programming