|
Descripción
|
|
|---|---|
| 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. | |
|
Internacional
|
Si |
|
JCR del ISI
|
Si |
|
Título de la revista
|
Journal of Signal Processing Systems For Signal Image And Video Technology |
|
ISSN
|
1939-8018 |
|
Factor de impacto JCR
|
1,023 |
|
Información de impacto
|
Datos JCR del año 2017 |
|
Volumen
|
|
|
DOI
|
10.1007/s11265-018-1380-9 |
|
Número de revista
|
|
|
Desde la página
|
1 |
|
Hasta la página
|
13 |
|
Mes
|
MAYO |
|
Ranking
|
|