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Descripción
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| 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 fixedpoint 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. | |
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Internacional
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Si |
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DOI
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10.1007/978-3-642-28565-0 |
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Edición del Libro
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Editorial del Libro
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Springer |
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ISBN
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978-3-642-28565-3 |
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Serie
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IFIP Advances in Information and Communication Technology |
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Título del Libro
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VLSI-SoC: Forward-Looking Trends in IC and Systems Design |
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Desde página
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182 |
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Hasta página
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205 |