Efficient function approximation for embedded and ASIC applications

In embedded systems and application specific integrated circuits (ASICs) that typically do not have a floating-point processor, measured data or function-sampled data is commonly described by means of an analytic function derived using standard numerical methods. The resultant errors are not caused by rounding the coefficients but by translating a real solution to a restricted fixed-point environment. A genetic algorithm has been constructed that discovers a superior piecewise polynomial approximation with coefficients restricted to the integer target space. This paper discusses the problem being solved and presents an overview of the implemented solution.