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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Vydané v:IEEE transactions on circuits and systems. II, Express briefs Ročník 58; číslo 9; s. 600 - 604
Hlavní autori: Rui Guo, DeBrunner, L. S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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  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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