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Proceedings Paper

A high-radix CORDIC architecture dedicated to compute the Gaussian potential function in neural networks
Author(s): Uwe H. Meyer-Baese; Anke Meyer-Baese; Javier Ramirez; Antonio Garcia
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Paper Abstract

In this paper, a new parallel hardware architecture dedicated to compute the Gaussian Potential Function is proposed. This function is commonly utilized in neural radial basis classifiers for pattern recognition as described by Lee; Girosi and Poggio; and Musavi et al. Attention to a simplified Gaussian Potential Function which processes uncorrelated features is confined. Operations of most interest included by the Gaussian potential function are the exponential and the square function. Our hardware computes the exponential function and its exponent at the same time. The contributions of all features to the exponent are computed in parallel. This parallelism reduces computational delay in the output function. The duration does not depend on the number of features processed. Software and hardware case studies are presented to evaluate the new CORDIC.

Paper Details

Date Published: 4 August 2003
PDF: 12 pages
Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); doi: 10.1117/12.486835
Show Author Affiliations
Uwe H. Meyer-Baese, Florida State Univ. (United States)
Anke Meyer-Baese, Florida State Univ. (United States)
Javier Ramirez, Univ. of Granada (Spain)
Antonio Garcia, Univ. of Granada (Spain)


Published in SPIE Proceedings Vol. 5103:
Intelligent Computing: Theory and Applications
Kevin L. Priddy; Peter J. Angeline, Editor(s)

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