Adaptive Bivariate Function Generation based on Chebyshev-Polynomials

Abstract

In this paper an adaptive hardware architecture for high-performance bivariate numerical function approximation is presented. Orthogonal Chebyshev-Polynomials are exploited that cover incremental accuracy refinements. Additionally, switching between two numeric functions is easily deployable by changing the set of polynomial coefficients. For evaluation, different configurations of the proposed hardware function generator are implemented and analyzed considering three bivariate numeric functions. The resulting performance highlights this approach to be a powerful extension for bivariate function approximation.