Complexity Reduction of Digital Filters Using Shift Inclusive Differential Coefficients

Choo, Hunsoo; Muhammad, K.; Roy, Kaushik

doi:10.1109/tsp.2004.827177

Cited by 32 publications

(40 citation statements)

References 14 publications

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“…While they used a graph-based approach, in terms of complexity reduction, CSE methods show better results. In [9], the authors reordered the computations and searched out common computations to maximize the sharing, but this method results in an increase in delay, corresponding to a delay of one adder step on average. The CSE algorithm used in [14] considers the redundancy in both the coefficients and MB, but the improvement of LO and LD over existing techniques was very small.…”

Section: Existing Methodsmentioning

confidence: 99%

“…The Bull-Horrocks modified (BHM) algorithm and n-dimensional reduced adder graph (RAGn) algorithm, presented in [17], reduce the number of LOs further. Graphrepresentation-based techniques reduce the number of LOs required to implement an MB compared to the CSE methods in [2], [4], [5], and [9]. However, the LDs of the resulting coefficient multipliers are considerably high.…”

Section: Existing Methodsmentioning

confidence: 99%

“…The number of super subexpressions grows with the word length; hence, it is useful only for filter coefficients of higher word lengths. Routing complexity is also high in [20] as compared to [2], [4]- [6], and [9]. Common subexpression elimination (SBE) comes in two varieties, a horizontal CS (HCS) type existing within a coefficient, and a vertical CS (VCS) type existing across the coefficients.…”

Section: Existing Methodsmentioning

confidence: 99%

“…They claimed that the algorithm produced the best reduction in number of LOs compared to the best known algorithm in the literature without increasing the LD requirement. The results in [1] (in terms of LOs and LD) have been compared with several CSE methods, such as Hartley [4], Pasko [5], BHM [17], C1 algorithm [22], MAG [19], NR-SCSE [6], HCUB [18], CSDC [21], and CRA-2 [15], [9]. The authors in [1] also presented design examples of several FIR filters using the proposed common subexpression algorithm.…”

Section: Comparisonmentioning

confidence: 99%

“…For different filter lengths (20,50,80,120,200,400), the coefficient word lengths were varied (12, 16, 20, 24 bits) to compare the LO and LD requirements. For filter lengths of 20,50,80,120,200, and 400, the average reductions in LO achieved by [1] as compared with [9] is 9.7%, while reductions of 12%, 13%, 18.5%, 5.6%, and 30% were achieved for CRA-2 [15], NR-SCSE [6], CSDC [21], MAG [19], and C1 [22], respectively. These comparison results are directly taken from [1].…”

Section: Comparisonmentioning

confidence: 99%

See 4 more Smart Citations

An Improved Non-CSD 2-Bit Recursive Common Subexpression Elimination Method to Implement FIR Filter

Hassan¹

2011

ETRI J

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The number of adders and critical paths in a multiplier block of a multiple constant multiplication based implementation of a finite impulse response (FIR) filter can be minimized through common subexpression elimination (CSE) techniques. A two-bit common subexpression (CS) can be located recursively in a noncanonic sign digit (CSD) representation of the filter coefficients. An efficient algorithm is presented in this paper to improve the elimination of a CS from the multiplier block of an FIR filter so that it can be realized with fewer adders and low logical depth as compared to the existing CSE methods in the literature. Vinod and others claimed the highest reduction in the number of logical operators (LOs) without increasing the logic depth (LD) requirement. Using the design examples given by Vinod and others, we compare the average reduction in LOs and LDs achieved by our algorithm. Our algorithm shows average LO improvements of 30.8%, 5.5%, and 22.5% with a comparative LD requirement over that of Vinod and others for three design examples. Improvement increases as the filter order increases, and for the highest filter order and lowest coefficient width, the LO improvements are 70.3%, 75.3%, and 72.2% for the three design examples.

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Section: Existing Methodsmentioning

confidence: 99%

Section: Existing Methodsmentioning

confidence: 99%

Section: Existing Methodsmentioning

confidence: 99%

Section: Comparisonmentioning

confidence: 99%

Section: Comparisonmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Non-CSD 2-Bit Recursive Common Subexpression Elimination Method to Implement FIR Filter

Hassan¹

2011

ETRI J

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Shift‐Add Circuits for Constant Multiplications

Meher¹,

Chang

Gustafsson

et al. 2017

Arithmetic Circuits for DSP Applications

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Digital IIR Filter Design with Fix-Point Representation Using Effective Evolutionary Local Search Enhanced Differential Evolution

Wang

Dong

Yan

et al. 2015

Computational Intelligence in Digital and Network Designs and Applications

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Previously, the parameters of digital IIR filters were encoded with floatingpoint representations. It is known that a fixed-point representation can effectively save computational resources and is more convenient for direct realization on hardware. Inherently, compared with floating-point representation, fixed-point representation may make the search space miss much useful gradient information and, therefore, raises new challenges. In this chapter, the universality of DE-based MA is improved by implementing more efficient evolutionary algorithms (EAs) as the local search techniques. The performance of the newly designed algorithm is experimentally verified in both function optimization tasks and digital IIR filter design problems.

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Complexity Reduction of Digital Filters Using Shift Inclusive Differential Coefficients

Cited by 32 publications

References 14 publications

An Improved Non-CSD 2-Bit Recursive Common Subexpression Elimination Method to Implement FIR Filter

An Improved Non-CSD 2-Bit Recursive Common Subexpression Elimination Method to Implement FIR Filter

Shift‐Add Circuits for Constant Multiplications

Digital IIR Filter Design with Fix-Point Representation Using Effective Evolutionary Local Search Enhanced Differential Evolution

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