Szczegóły publikacji
Opis bibliograficzny
Comparison of GPU and FPGA implementation of SVM algorithm for fast image segmentation / Marcin PIETROŃ, Maciej WIELGOSZ, Dominik ŻUREK, Ernest JAMRO, Kazimierz WIATR // W: ARCS 2013 : Architecture of Computing Systems : 26th international conference : Prague, Czech Rebulic, February 19–22, 2013 : proceedings / eds. Hana Kubátová, [et al.]. — Berlin ; Heidelberg : Springer-Verlag, cop. 2013. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 7767). — ISBN: 978-3-642-36423-5; e-ISBN: 978-3-642-36424-2. — S. 292–302. — Bibliogr. s. 302
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 79619 |
|---|---|
| Data dodania do BaDAP | 2014-02-06 |
| Rok publikacji | 2013 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 26th International Conference on Architecture of Computing Systems |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
This paper presents preliminary implementation results of the SVM (Support Vector Machine) algorithm. SVM is a dedicated mathematical formula which allows us to extract selective objects from a picture and assign them to an appropriate class. Consequently, a black and white images reflecting an occurrence of the desired feature is derived from an original picture fed into the classifier. This work is primarily focused on the FPGA and GPU implementations aspects of the algorithm as well as on comparison of the hardware and software performance. A human skin classifier was used as an example and implemented both on Intel Xeon E5645.40 GHz, Xilinx Virtex-5 LX220 and Nvidia Tesla m2090. It is worth emphasizing that in case of FPGA implementation the critical hardware components were designed using HDL (Hardware Description Language), whereas the less demanding or standard ones such as communication interfaces, FIFO, FSMs were implemented in Impulse C. Such an approach allowed us both to cut a design time and preserve a high performance of the hardware classification module. In case of GPU implementation whole algorithm is implemented in CUDA.
