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Two High-Performance Adaptive Filter Implementation Schemes Using Distributed Arithmetic
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DOI：10.1109/TCSII.2011.2161168.png)
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Abstract
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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.
Keywords:
Adaptive filter
distributed arithmetic (DA)
finite-impluse response (FIR)
least mean square (LMS)
lookup table (LUT)
multiply accumulate ( MAC)
offset-binary coding (OBC)
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