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Memorias de investigación
Communications at congresses:
Fast Fixed-Point Optimization of DSP Algorithms
Year:2010
Research Areas
  • Microelectronics
Information
Abstract
In this paper, a fixed-point quantization noise estimator aiming at Digital Signal Processing (DSP) algorithms is presented. The estimator enables significant reduction in the computation time required to perform complex word-length optimizations while providing a high accuracy. Affine Arithmetic (AA) is used to provide a Signal-to-Quantization Noise-Ratio (SQNR) estimation for 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 to relate the fixed-point formats of the signals with the output noise distribution by means of fast matrix operations. Thus, a fast estimation is achieved and the word-length optimization computation time is significantly reduced. The estimator is tested using a subset of non-linear algorithms such as vector operations, power computation IIR filter, adaptive filters, and channel equalizers. The Word-length optimization computation time is boosted by three orders of magnitude while keeping the average estimation error down to 6% for most cases.
International
Si
Congress
IEEE/IFIP International Conference on VLSI and System-on-Chip
960
Place
Madrid (España)
Reviewers
Si
ISBN/ISSN
978-1-4244-6469-2
10.1109/VLSISOC.2010.5642659
Start Date
27/09/2010
End Date
29/09/2010
From page
195
To page
200
Proceedings of the 18th IEEE/IFIP International Conference on VLSI and System-on-Chip, VLSI-SoC'10
Participants
  • Autor: Gabriel Caffarena Fernandez (UPM)
  • Autor: Juan Antonio Lopez Martin (UPM)
  • Autor: Angel Fernandez Herrero (UPM)
  • Autor: Carlos Carreras Vaquer (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Laboratorio de Sistemas Integrados (LSI)
  • Departamento: Ingeniería Electrónica
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