An algorithm for extremal eigenvectors computation of Hermitian matrices and its FPGA implementation - ENSTA Bretagne - École nationale supérieure de techniques avancées Bretagne Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

An algorithm for extremal eigenvectors computation of Hermitian matrices and its FPGA implementation

Résumé

We consider the problem of implementing an algorithm for the extraction of leading eigenvectors of a small Hermitian matrix on field-programmable gate array (FPGA). The evolution of FPGAs can now handle increasingly bandwidth problems or larger in size. Jacobi algorithms are usually implemented in FPGA for real matrix size not exceeding 20*20. The increase in size or complex number problem may lead to use other algorithms such as Lanczos, which are rarely implemented on FPGA. Recently, it has been pointed out that the Lanczos method can efficiently address the extreme eigenvalues computation problem on FPGA, for medium size real matrices. This paper presents an algorithm for the extraction of extremal eigenvalues and corresponding eigenvectors for small Hermitian matrix using a high-level approach for the architecture synthesis.
Fichier principal
Vignette du fichier
G_LUCIUS_mwscas2013_An_algorithm_for_extremal_eigenvectors_computation_of_Hermitian_matrices_and_its_ FPGA_implementation (1).pdf (237.45 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01508692 , version 1 (14-12-2018)

Identifiants

Citer

Guillaume Lucius, Frédéric Le Roy, Denis Aulagnier, Stéphane Azou. An algorithm for extremal eigenvectors computation of Hermitian matrices and its FPGA implementation. IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2013), Aug 2013, Columbus, OH, United States. ⟨10.1109/MWSCAS.2013.6674920⟩. ⟨hal-01508692⟩
202 Consultations
537 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More