Two High-Performance Adaptive Filter Implementation Schemes Using Distributed Arithmetic

Distributed arithmetic (DA) is performed to design bit-level architectures for vector-vector multiplication with a direct application for the implementation of convolution, which is necessary for digital filters. In this brief, two novel DA-based implementation schemes are proposed for adaptive fini...

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Published in:	IEEE transactions on circuits and systems. II, Express briefs Vol. 58; no. 9; pp. 600 - 604
Main Authors:	Rui Guo, DeBrunner, L. S.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.09.2011 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive filter Adaptive filters Arithmetic Circuits Design engineering Digital filters Digital signal processing distributed arithmetic (DA) Encoding Finite impulse response filter finite-impluse response (FIR) High speed least mean square (LMS) Least squares approximation lookup table (LUT) Lookup tables Mean square values multiply accumulate (MAC) offset-binary coding (OBC) Read only memory Speech processing Table lookup
ISSN:	1549-7747, 1558-3791
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Abstract	Distributed arithmetic (DA) is performed to design bit-level architectures for vector-vector multiplication with a direct application for the implementation of convolution, which is necessary for digital filters. In this brief, two novel DA-based implementation schemes are proposed for adaptive finite-impulse response filters. Different from conventional DA techniques, our proposed schemes use coefficients as addresses to access a series of lookup tables (LUTs) storing sums of delayed and scaled input samples. Two smart LUT updating methods are developed, and least-mean-square adaptation is performed to update the weights and minimize the mean square error between the estimated and desired output. Results show that our two high-performance designs achieve high speed, low computation complexities, and low area cost.
AbstractList	Distributed arithmetic (DA) is performed to design bit-level architectures for vector-vector multiplication with a direct application for the implementation of convolution, which is necessary for digital filters. In this brief, two novel DA-based implementation schemes are proposed for adaptive finite-impulse response filters. Different from conventional DA techniques, our proposed schemes use coefficients as addresses to access a series of lookup tables (LUTs) storing sums of delayed and scaled input samples. Two smart LUT updating methods are developed, and least-mean-square adaptation is performed to update the weights and minimize the mean square error between the estimated and desired output. Results show that our two high-performance designs achieve high speed, low computation complexities, and low area cost.
Author	DeBrunner, L. S. Rui Guo
Author_xml	– sequence: 1 surname: Rui Guo fullname: Rui Guo email: rg07g@fsu.edu organization: Dept. of Electr. & Comput. Eng., Florida State Univ., Tallahassee, FL, USA – sequence: 2 givenname: L. S. surname: DeBrunner fullname: DeBrunner, L. S. email: linda.debrunner@fsu.edu organization: Dept. of Electr. & Comput. Eng., Florida State Univ., Tallahassee, FL, USA
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SubjectTerms	Adaptive filter Adaptive filters Arithmetic Circuits Design engineering Digital filters Digital signal processing distributed arithmetic (DA) Encoding Finite impulse response filter finite-impluse response (FIR) High speed least mean square (LMS) Least squares approximation lookup table (LUT) Lookup tables Mean square values multiply accumulate (MAC) offset-binary coding (OBC) Read only memory Speech processing Table lookup
Title	Two High-Performance Adaptive Filter Implementation Schemes Using Distributed Arithmetic
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