SQNR Estimation of Fixed-Point DSP Algorithms. (20th May 2010)

• Record Type: Journal Article
• Authors: Caffarena Caffarena, Gabriel Gabriel; Carreras Carreras, Carlos Carlos; López López, Juan A. Juan A.; Fernández Fernández, Ángel Ángel
• Other Names: Yang Yang Jar Ferr Jar Ferr Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-05-20
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/171027
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : A fast and accurate quantization noise estimator aiming at fixed-point implementations of Digital Signal Processing (DSP) algorithms is presented. The estimator enables significant reduction in the computation time required to perform complex word-length optimizations. The proposed estimator is based on the use of Affine Arithmetic (AA) and it is presented in two versions: (i) a general version suitable for differentiable nonlinear algorithms, and Linear Time-Invariant (LTI) algorithms with and without feedbacks; and (ii) an LTI optimized version. The process relies on the parameterization of the statistical properties of the noise at the output of fixed-point algorithms. Once the output noise is parameterized (i.e., related to the fixed-point formats of the algorithm signals), a fast estimation can be applied throughout the word-length optimization process using as a precision metric the Signal-to-Quantization Noise Ratio (SQNR). The estimator is tested using different LTI filters and transforms, as well as a subset of non-linear operations, such as vector operations, adaptive filters, and a channel equalizer. Fixed-point optimization times are boosted by three orders of magnitude while keeping the average estimation error down to 4%.