Exact and Approximate Algorithms for the Filter Design Optimization Problem

Abstract

The filter design optimization (FDO) problem is defined as finding a set of filter coefficients that yields a filter design with minimum complexity, satisfying the filter constraints. It has received a tremendous interest due to the widespread application of filters. Assuming that the coefficient multiplications in the filter design are realized under a shift-adds architecture, the complexity is generally defined in terms of the total number of adders and subtractors. In this paper, we present an exact FDO algorithm that can guarantee the minimum design complexity under the minimum quantization value, but can only be applied to filters with a small number of coefficients. We also introduce an approximate algorithm that can handle filters with a large number of coefficients using less computational resources than the exact FDO algorithm and find better solutions than existing FDO heuristics. We describe how these algorithms can be modified to handle a delay constraint in the shift-adds designs of the multiplier blocks and to target different filter constraints and filter forms. Experimental results show the effectiveness of the proposed algorithms with respect to prominent FDO algorithms and explore the impact of design parameters, such as the filter length, quantization value, and filter form, on the complexity and performance of filter designs.