Finite impulse response (FIR) filter

Abstract

The invention discloses a finite impulse response (FIR) filter, which consists of a filtering coefficient buffer, a to-be-filtered data buffer, a multiplier, an accumulator, an accumulating register and a comparator, wherein the filtering coefficient buffer is used for buffering coefficients which are used in the filter; the to-be-filtered data buffer is used for buffering data to be filtered; the multiplier is used for computing a product of a single filter coefficient multiplied by BS data to be filtered; the accumulator is used for computing a sum of the output of the multiplier and the output of the accumulating register; the accumulating register is used for storing a result of the accumulator; and the output of the comparator is used for indicating whether the current output of the accumulator is filtering output. By adoption of the FIR filter, high efficiency of parallel computation can be realized.

Description

technical field [0001] The invention relates to the technical field of digital signal processing in the electronics industry, in particular to a finite impulse response (Finite Impulse Response, FIR for short) filter based on a multiply-accumulator. Background technique [0002] FIR filters are widely used in digital communication systems, and the industry has been working on improving their filtering performance. This filter can be used to perform various tasks such as matched filtering, noise isolation, channel equalization, etc. [0003] In general, the FIR filter algorithm can be expressed as: [0004] Y → ( k ) = Σ i = 0 K - 1 H → ( i ) X ...

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Problems solved by technology

[0008] 1) When the number of filter coefficients K is large, the FIR filter in the prior art generally multiplies each filter coefficient with the data to be filtered to obtain sub-results. This process is completed in parallel, but then the sub-results are added to obtain the filtered result. When the results are obtained, the time required for the accumulation directly depends on the number of filter coefficients. When the number of filter coefficients is large, the critical path for the output of the filter results is very long, which seriously affects its work efficiency;

[0009] 2) The number of digital processing units is limited by the number of filter coefficients. Only after the number of filter coefficients in the application is determined can the number of digital processing units be fixed, which lacks application flexibility.

[0010] 3) In one-dimensional filtering applications, there is a principle of locality in the data to be filtered. Loading repeated data makes the use efficiency of the data to be filtered very low. In addition, repeated access to the memory will have a large time overhead on the one hand, and on the other hand will increase the power consumption of the system

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