We present an improved algorithm for selecting variable precision coefficients for FIR filters that produces a reduced space implementation with no degradation in frequency response. The algorithm is based on the fact that the frequency response of a filter has different sensitivities to different coefficients depending on the response itself (i.e. the coefficient value). The method provided here makes it possible to predict the variable precision of the quantized coefficients that are required to meet the specification. This approach, along with other technologies such as CSD and the scaling method used in our realizations, has opened exciting implementation possibilities for FPGAs, ASICs, and custom VLSI. The example study in this paper shows that using variable precision to exploit redundancy across the coefficients results in significant reductions in complexity and area over the uniform wordlength method.
We propose a method for designing a filter to meet a set of specifications, which can be implemented with reduced area. Our approach combines a prefilter implementation structure whose function has been developed over the years with our previously reported variable precision technique for intelligently quantizing the filter coefficients. Our area-efficient structure uses frequency masking with a single filter model to give good performance with low order and low coefficient sensitivity. Our method does use a novel connection of a simple prefilter structure used in a frequency masking technique to give good designs for the previously unattainable wide-band filter designs. The technique also gives designs with superior (vely sharp) transition regions. Both types produce filters that efficiently use digital circuitry, leading to space-efficient designs that are significantly smaller than would otherwise be the case. Some examples are given to demonstrate the effectiveness of our design.
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