SQNR Estimation of Fixed-Point DSP Algorithms

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%.