A Case Study Of The Stochastic Modeling Approach For Range Estimation

Abstract : The floating-point to fixed-point conversion is an important part of the hardware design in order to obtain efficient implementations. In order to optimize the integer word-length under performance constraints, the dynamic variations of the variables during execution must be determined. Traditional range estimation methods based on simulations are data dependent and time consuming whereas analytical methods like interval and affine arithmetic give pessimistic results as they lack of a statistical background. Recently, a novel approach, based on the Karhunen-Loève Expansion (KLE) was presented for linear time-invariant (LTI) systems offering a solid stochastic foundation. Our paper presents an implementation of this theory and shows its efficiency for an OFDM modulator test case study. We also present a review of the uncertainty quantifications problem and the different phases of the range estimation methodology.
Type de document :
Communication dans un congrès
Design and Architectures for Signal and Image Processing, Oct 2010, Edinburgh, United Kingdom. pp.301-308, 2010
Liste complète des métadonnées

https://hal.inria.fr/inria-00554268
Contributeur : Emmanuel Casseau <>
Soumis le : lundi 10 janvier 2011 - 15:33:39
Dernière modification le : mercredi 16 mai 2018 - 11:23:26

Identifiants

  • HAL Id : inria-00554268, version 1

Citation

Andrei Banciu, Emmanuel Casseau, Daniel Menard, Thierry Michel. A Case Study Of The Stochastic Modeling Approach For Range Estimation. Design and Architectures for Signal and Image Processing, Oct 2010, Edinburgh, United Kingdom. pp.301-308, 2010. 〈inria-00554268〉

Partager

Métriques

Consultations de la notice

835