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Title: Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing
Authors: Lazcano, R.
Madroñal, D.
Fabelo, H. 
Ortega, S. 
Salvador, R.
Callico, G. M. 
Juarez, E.
Sanz, C.
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Hyperspectral imaging
Massively parallel processing
Real-time processing
Parallel programming
Issue Date: 2018
Publisher: 1939-8018
Journal: Journal of Signal Processing Systems 
Abstract: 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.
ISSN: 1939-8018
DOI: 10.1007/s11265-018-1380-9
Source: Journal of Signal Processing Systems[ISSN 1939-8018], p. 1-13
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