An Overflow Free Fixed-point Eigenvalue Decomposition Algorithm: Case Study of Dimensionality Reduction in Hyperspectral Images

Abstract : We consider the problem of enabling robust range estimation of eigenvalue decomposition (EVD) algorithm for a reliable fixed-point design. The simplicity of fixed-point circuitry has always been so tempting to implement EVD algorithms in fixed-point arithmetic. Working towards an effective fixed-point design, integer bit-width allocation is a significant step which has a crucial impact on accuracy and hardware efficiency. This paper investigates the shortcomings of the existing range estimation methods while deriving bounds for the variables of the EVD algorithm. In light of the circumstances, we introduce a range estimation approach based on vector and matrix norm properties together with a scaling procedure that maintains all the assets of an analytical method. The method could derive robust and tight bounds for the variables of EVD algorithm. The bounds derived using the proposed approach remain same for any input matrix and are also independent of the number of iterations or size of the problem. Some benchmark hyperspectral data sets have been used to evaluate the efficiency of the proposed technique. It was found that by the proposed range estimation approach, all the variables generated during the computation of Jacobi EVD is bounded within ±1.
Type de document :
Communication dans un congrès
2017 Conference On Design And Architectures For Signal And Image Processing (DASIP). , Sep 2017, Dresden, Germany. 0028, 〈http://dasip2017.esit.rub.de/program.html〉
Liste complète des métadonnées

Littérature citée [42 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01635958
Contributeur : Bibek Kabi <>
Soumis le : jeudi 30 novembre 2017 - 14:42:57
Dernière modification le : samedi 21 juillet 2018 - 16:58:02

Fichier

1711.10600.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01635958, version 1

Citation

Bibek Kabi, Anand S Sahadevan, Tapan Pradhan. An Overflow Free Fixed-point Eigenvalue Decomposition Algorithm: Case Study of Dimensionality Reduction in Hyperspectral Images. 2017 Conference On Design And Architectures For Signal And Image Processing (DASIP). , Sep 2017, Dresden, Germany. 0028, 〈http://dasip2017.esit.rub.de/program.html〉. 〈hal-01635958〉

Partager

Métriques

Consultations de la notice

264

Téléchargements de fichiers

108