Fast Fixed-Point Optimization of DSP Algorithms

  • Gabriel Caffarena
  • Ángel Fernández-Herrero
  • Juan A. López
  • Carlos Carreras
Conference paper

DOI: 10.1007/978-3-642-28566-0_8

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 373)
Cite this paper as:
Caffarena G., Fernández-Herrero Á., López J.A., Carreras C. (2012) Fast Fixed-Point Optimization of DSP Algorithms. In: Ayala J.L., Atienza Alonso D., Reis R. (eds) VLSI-SoC: Forward-Looking Trends in IC and Systems Design. VLSI-SoC 2010. IFIP Advances in Information and Communication Technology, vol 373. Springer, Berlin, Heidelberg

Abstract

In this chapter, the fast fixed-point optimization of Digital Signal Processing (DSP) algorithms is addressed. A fast quantization noise estimator is presented. The estimator enables a significant reduction in the computation time required to perform complex fixed-point optimizations, while providing a high accuracy. Also, a methodology to perform fixed-point optimization is developed.

Affine Arithmetic (AA) is used to provide a fast Signal-to-Quantization Noise-Ratio (SQNR) estimation that can be used during the fixed-point optimization stage. The fast estimator covers differentiable non-linear algorithms with and without feedbacks. The estimation is based on the parameterization of the statistical properties of the noise at the output of fixed-point algorithms. This parameterization allows relating the fixed-point formats of the signals to the output noise distribution by means of fast matrix operations. Thus, a fast estimation is achieved and the computation time of the fixed-point optimization process is significantly reduced.

The proposed estimator and the fixed-point optimization methodology are tested using a subset of non-linear algorithms, such as vector operations, IIR filter for mean power computation, adaptive filters – for both linear and non-linear system identification – and a channel equalizer. The computation time of fixed-point optimization is boosted by three orders of magnitude while keeping the average estimation error down to 6% in most cases.

Keywords

Fixed-Point Optimization Digital Signal Processing Quantization Word-Length Affine Arithmetic Error Estimation Signal-to-Quantization-Noise Ratio 
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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Gabriel Caffarena
    • 1
  • Ángel Fernández-Herrero
    • 2
  • Juan A. López
    • 2
  • Carlos Carreras
    • 2
  1. 1.Dep. Ingeniería de Sistemas de Información y TelecomunicaciónUniversidad CEU San PabloMadridSpain
  2. 2.Dep. Ingeniería ElectrónicaUniversidad Politécnica de MadridMadridSpain

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