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Abstract
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| 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. | |
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International
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Si |
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Congress
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IEEE/IFIP International Conference on VLSI and System-on-Chip |
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960 |
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Place
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Madrid (España) |
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Reviewers
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Si |
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ISBN/ISSN
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978-1-4244-6469-2 |
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10.1109/VLSISOC.2010.5642659 |
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Start Date
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27/09/2010 |
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End Date
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29/09/2010 |
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From page
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195 |
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To page
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200 |
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Proceedings of the 18th IEEE/IFIP International Conference on VLSI and System-on-Chip, VLSI-SoC'10 |