A parallel CORDIC architecture dedicated to compute the Gaussian potential function in neural networks

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 [(Neural Networks for Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, 1992)], Girosi and Poggio [(Neural Comput. 1 (1989) 465)], and Musavi et al. (Neural Networks 5 (1992) 595). 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 functions. 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.