Automated design of low complexity FIR filters

Abstract

This paper considers the design of low complexity FIR filters. Complexity is reduced by constraining the filters to have integer coefficients, which can be efficiently implemented using primitive operator directed graphs (PODG). Genetic Algorithms (GAS) are used in conjunction with a heuristic graph design algorithm, to provide a solution set which represents different compromises between performance, complexity and filter order.Example results are presented for both one and two dimensional filters, and are shown to provide both superior performance and complexity, compared to previous methods. The main benefits result from the use of a joint optimization, rather than a separable 2-stage approach. The use of a PODG representation is shown to provide significant improvements over a canonic signed digit (CSD) or signed power-of-two (SPT) representation.

Hardware based genetic evolution of self-adaptive arbitrary response FIR filters

“…This simplifies the filter design without mitigating its performance. This design approach has been explored in many recent research works [1,6,21]. This has also involved exploration of the design using evolutionary algorithms.…”

“…Genetic filter designs have also been of considerable interest in recent years. Genetic evolution (application of several instances of a given GA) of FIR filters has been of considerable interest in the recent past [1,6,18,20,21]. However, the focus in each of these has been discrete and diverse.…”

“…Insights into approximations and simplified heuristics are essential for effective hardware translation. In [21], Redmill and Bull use the Primitive Operator Directed Graph (PODG) representation to simplify the design. The GA is used to provide a Non Dominated Set (NDS) of solutions.…”