Adaptive linear prediction with power-of-two coefficients

When digital filters are designed with power-of-two coefficients, the multiplications can be implemented by simple shifting operations. For VLSI implementations, multiplierless filters are faster and more compact than filters with multipliers. In this paper, an algorithm is designed to find and update the power-of-two coefficients of an adaptive filter. The new method uses the well known genetic algorithm (GA) for this purpose. The GA is used in a unique way in order to reduce computations. Small blocks of data are used for the GA and only one new generation is produced per sample of data. This coupled with fact that the coefficients are power-of-two, yields a computational complexity of O(N) additions and no multiplications. Examples are given for adaptive linear prediction. The results are very promising and illustrate the performance of the new algorithm.