A systematic algorithm for the design of multiplierless FIR filters

Yli‐Kaakinen, Juha; Saramäki, T.

doi:10.1109/iscas.2001.921038

Cited by 66 publications

(83 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make the formulation of the optimization linear, it is beneficial to represent the SPT terms using 0/1 variables as and the objective function can be formulated as 25) where M = N 2 + 1 for a type-I FIR filter. These techniques of reducing the number of SPT terms has been further extended to combine with sub-expression sharing where the filters are designed such that not only the number of SPT terms are minimized, the resulting shiftadd network is also shared optimally [77,78,259]. …”

Section: Fir Filter Design By Linear Programmingmentioning

confidence: 99%

Techniques for Efficient Implementation of FIR and Particle Filtering

Alam

2016

View full text Add to dashboard Cite

Finite-length impulse response (FIR) filters occupy a central place many signal processing applications which either alter the shape, frequency or the sampling frequency of the signal. FIR filters are used because of their stability and possibility to have linear-phase but require a high filter order to achieve the same magnitude specifications as compared to infinite impulse response (IIR) filters. Depending on the size of the required transition bandwidth the filter order can range from tens to hundreds to even thousands. Since the implementation of the filters in digital domain requires multipliers and adders, high filter orders translate to a large number of these arithmetic units for its implementation. Research towards reducing the complexity of FIR filters has been going on for decades and the techniques used can be roughly divided into two categories; reduction in the number of multipliers and simplification of the multiplier implementation.One technique to reduce the number of multipliers is to use cascaded subfilters with lower complexity to achieve the desired specification, known as frequency-response masking (FRM). One of the sub-filters is a upsampled model filter whose band edges are an integer multiple, termed as the period L, of the target filter's band edges. Other sub-filters may include complement and masking filters which filter different parts of the spectrum to achieve the desired response. From an implementation point-of-view, time-multiplexing is beneficial because generally the allowable maximum clock frequency supported by the current state-of-the-art semiconductor technology does not correspond to the application bound sample rate. A combination of these two techniques plays a significant role towards efficient implementation of FIR filters. Part of the work presented in this dissertation is architectures for time-multiplexed FRM filters that benefit from the inherent sparsity of the periodic model filters.These time-multiplexed FRM filters not only reduce the number of multipliers but lowers the memory usage. Although the FRM technique requires a higher number delay elements, it results in fewer memories and more energy efficient memory schemes when time-multiplexed. Different memory arrangements and memory access schemes have also been discussed and compared in terms of their efficiency when using both single and dual-port memories. An efficient v vi Abstract pipelining scheme has been proposed which reduces the number of pipelining registers while achieving similar clock frequencies. The single optimal point where the number of multiplications is minimum for non-time-multiplexed FRM filters is shown to become a function of both the period, L and time-multiplexing factor, M . This means that the minimum number of multipliers does not always correspond to the minimum number of multiplications which also increases the flexibility of implementation. These filters are shown to achieve power reduction between 23% and 68% for the considered examples.To simplify the multiplier, alt...

show abstract

Section: Fir Filter Design By Linear Programmingmentioning

confidence: 99%

Techniques for Efficient Implementation of FIR and Particle Filtering

Alam

2016

View full text Add to dashboard Cite

show abstract

“…In the sequel, CSD (R, P R ) denotes the space of the CSD numbers where the maximum number of signed-powers-of-two terms and the maximum number of fractional bits are R and P R , respectively. When finding the optimized simple discrete-value representation forms for the impulse-response coefficients of FIR filters, it is a common practice to accomplish the optimization so that the scaled response meets the given magnitude criteria as proposed in [12], [13], [31], [35]. In this case, the criterion, as given by equation (6b), is replaced by…”

Section: Coefficient Representation Form Under Considerationmentioning

confidence: 99%

“…When generating a parameter space for infinite-precision coefficients including the feasible space where the infinite-precision overall system meets the given criteria, it is beneficial to mimic the scheme discovered by us in [31] for quantizing the coefficient values of a single linear-phase FIR filter. Based on this discovery, a very straightforward quantization scheme for the coefficients is obtained as follows.…”

Section: Feasible Parameter Space For the Infinite-precision Filter Smentioning

confidence: 99%

“…In the second case, the common subexpressions within all the quantized coefficient values are shared in order to reduce the implementation cost even more [2], [3], [6], [7], [14], [18]- [20]. For achieving the above-mentioned targets, the efficient quantization scheme that we proposed in [31] for obtaining these targets in the case of a single linear-phase FIR filter is properly modified. The main difference is that for the modified Farrow structure, there are several parallel fixed FIR filters, and the common subexpressions should be properly shared between all these filters when implementing them using the direct-form structure exploiting the coefficient symmetries, as shown in Figure 1, and when properly sharing the same 2M − 1 delay elements between the filter blocks.…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing the above-mentioned facts simultaneously reduces the implementation cost and the number of coefficient values to be optimized. The third step is based on the following observation made by us in [31] when quantizing the coefficient values of a conventional linear-phase FIR filter. In order to find the best finite-precision solution, it has turned out to be very beneficial to first find the smallest and largest values for all the filter coefficients such that the given criteria are still met by reoptimizing the remaining coefficient values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiplication-Free Polynomial-Based FIR Filters with an Adjustable Fractional Delay

Yli‐Kaakinen

Saramäki

2006

Circuits Syst Signal Process

View full text Add to dashboard Cite

An efficient coefficient quantization scheme is described for minimizing the cost of implementing fixed parallel linear-phase finite impulse response (FIR) filters in the modified Farrow structure introduced by Vesma and Saramäki for generating FIR filters with an adjustable fractional delay. The implementation costs under consideration are the minimum number of adders and subtracters when implementing these parallel subfilters as a very large-scale integration (VLSI) circuit. Two implementation costs are under consideration to meet the given criteria. In the first case, all the coefficient values are implemented independently of each other as a few signed-powers-of-two terms, whereas in the second case, the common subexpressions within all the coefficient values included in the overall implementation are properly shared in order to reduce the overall implementation cost even further. The optimum finite-precision solution is found in four steps. First, the number of filters and their (common odd) order are determined such that the given criteria are sufficiently exceeded in order to allow some coefficient quantization errors. Second, those coefficient values of the subfilters having a negligible effect on the overall system performance are fixed to be zero valued. In addition, the experimentally observed attractive connections between the coefficient values of the subfilters, after setting some coefficient values equal to zero, are utilized to reduce both the implementation cost and the parameters to be optimized even more. Third, constrained nonlinear optimization is applied to determine for the remaining infinite-precision coefficients a parameter space that includes the feasible space where the given criteria are met. The fourth step involves finding in this space the desired finite-precision coefficient values for minimizing the given implementation costs to meet the stated overall criteria. Several examples are included illustrating the efficiency of the proposed synthesis scheme. *

show abstract

Design of an Efficient Flexible Architecture for Color Image Enhancement

Zhang

Seow

et al. 2006

Advances in Computer Systems Architecture

View full text Add to dashboard Cite

A systematic algorithm for the design of multiplierless FIR filters

Cited by 66 publications

References 11 publications

Techniques for Efficient Implementation of FIR and Particle Filtering

Techniques for Efficient Implementation of FIR and Particle Filtering

Multiplication-Free Polynomial-Based FIR Filters with an Adjustable Fractional Delay

Design of an Efficient Flexible Architecture for Color Image Enhancement

Contact Info

Product

Resources

About