Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/46809
Título: Energy consumption characterization of a Massively Parallel Processor Array (MPPA) platform running a hyperspectral SVM classifier
Autores/as: Madronal, D.
Lazcano, R.
Fabelo, H. 
Ortega, S. 
Salvador, R.
Callico, G. M. 
Juarez, E.
Sanz, C.
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Hyperspectral imaging
Parallel processing
Support vector machines
Energy consumption
Powe dissipation, et al.
Fecha de publicación: 2017
Publicación seriada: 2017 Conference On Design And Architectures For Signal And Image Processing (Dasip)
Conferencia: 2017 Conference on Design and Architectures for Signal and Image Processing, DASIP 2017 
Resumen: In this paper, a Massively Parallel Processor Array platform is characterized in terms of energy consumption using a Support Vector Machine for hyperspectral image classification. This platform gathers 16 clusters composed of 16 cores each, i.e., 256 processors working in parallel. The objective of the work is to associate power dissipation and energy consumed by the platform with the different resources of the architecture. Experimenting with a hyperspectral SVM classifier, this study has been conducted using three strategies: i) modifying the number of processing elements, i.e., clusters and cores, ii) increasing system frequency, and iii) varying the number of active communication links during the analysis, i.e., I/Os and DMAs. As a result, a relationship between the energy consumption and the active platform resources has been exposed using two different parallelization strategies. Finally, the implementation that fully exploits the parallelization possibilities working at 500MHz has been proven to be also the most efficient one, as it reduces the energy consumption by 98% when compared to the sequential version running at 400MHz.
URI: http://hdl.handle.net/10553/46809
ISBN: 9781538635346
ISSN: 2164-9766
DOI: 10.1109/DASIP.2017.8122112
Fuente: Conference on Design and Architectures for Signal and Image Processing, DASIP[ISSN 2164-9766],v. 2017-September, p. 1-6
Colección:Actas de congresos
Vista completa

Citas SCOPUSTM   

5
actualizado el 24-mar-2024

Visitas

72
actualizado el 02-mar-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.