No abstract

No abstract

In this paper, a novel optimization technique is proposed to optimize filter coefficients of linear phase finite-impulse response (FIR) filter to share common subexpressions within and among coefficients. Existing approaches of common subexpression elimination optimize digital filters in two stages: first, an FIR filter is designed in a discrete space such as finite wordlength space or signed power-of-two (SPT) space to meet a given specification; in the second stage, an optimization algorithm is applied on the discrete coefficients to find and eliminate the common subexpressions. Such a two-stage optimization technique suffers from the problem that the search space in the second stage is limited by the finite wordlength or SPT coefficients obtained in the first stage optimization. The new proposed algorithm overcomes this problem by optimizing the filter coefficients directly in subexpression space for a given specification. Numerical examples of benchmark filters show that the required number of adders obtained using the proposed algorithm is much less than those obtained using two-stage optimization approaches.

The most advanced techniques in the design of multiplierless finite impulse response (FIR) filters explore common subexpression sharing when the filter coefficients are optimized. Existing techniques, however, either suffer from a heavy computational overhead, or have no guarantees on the minimal hardware cost in terms of the number of adders. A recent technique capable of designing long filters optimizes filter coefficients in pre-specified subexpression spaces. The pre-specified subexpression spaces determine if a filter with fewer adders may be achieved. Unfortunately, there is no known technique that can find subexpression spaces that can guarantee the solution with the minimum number of adders in the implementation. In this paper, a tree search algorithm is proposed to update and expand the subexpression spaces dynamically, and thus, to achieve the maximum subexpression sharing during the optimization. Numerical examples show that the proposed algorithm generates filters using fewer adders than other non-optimum algorithms. On the other hand, as a consequence of its efficiency, our proposed technique is able to design longer filters than the global optimum algorithm.

No abstract

It is well known that filters designed using the frequency response masking (FRM) technique have very sparse coefficients. The number of nontrivial coefficients of a digital filter designed using the FRM technique is only a very small fraction of that of a minimax optimum design meeting the same set of specifications. A digital filter designed using FRM technique is a network of several subfilters. Several methods have been developed for optimizing the subfilters. The earliest method optimizes the subfilters separately and produces a network of subfilters with excellent finite word-length performance. Subsequent techniques optimize the subfilters jointly and produce filters with significantly smaller numbers of nontrivial coefficients. Unfortunately, these joint optimization techniques, that optimize only the overall frequency response characteristics, may produce filters with undesirable finite word-length properties. The design of FRM-based filters that simultaneously optimizes the frequency response and finite wordlength properties had not been reported in the literatures. In this paper, we develop several new optimization approaches that include the finite word-length properties of the overall filter into the optimization process. These new approaches produce filters with excellent finite word-length performance with almost no degradation in frequency response performance.

No abstract

A novel genetic algorithm (GA) for the design of a canonical signed powerof-two (SPT) coefficient lattice structure quadrature mirror filter bank is presented in this paper. Genetic operations may render the SPT representation of a value noncanonical. In this paper, a new encoding scheme is introduced to encode the SPT values. In this new scheme, the canonical property of the SPT values is preserved under genetic operations. Additionally, two new features that drastically improve the performance of our GA are introduced. (1) An additional level of natural selection is introduced to simulate the effect of natural selection when sperm cells compete to fertilize an ovule; this dramatically improves the offspring survival rate. A conventional GA is analogous to intracytoplasmic sperm injection and has an extremely low offspring survival rate, resulting in very slow convergence.(2) The probability of mutation for each codon of a chromosome is weighted by the reciprocal of its effect. Because of these new features, the performance of our new GA outperforms conventional GAs.