VLSI Architecture for Enhanced Approximate Message Passing Algorithm

Compressed sensing (CS) enables the reconstruction of sparse signals from a small number of linear measurements. However, sparse signal recovery algorithms require significant computational effort, even for problems of moderate size, and make their hardware realization a highly challenging task. In...

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Veröffentlicht in:IEEE transactions on circuits and systems for video technology Jg. 30; H. 9; S. 3253 - 3267
Hauptverfasser: Batta, Kota Naga Srinivasaro, Chakrabarti, Indrajit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
3D wavelets
Algorithms
approximate message passing (AMP)
Approximation algorithms
Architecture
CMOS
Coders
Compressed sensing
compressed sensing (CS)
Computer architecture
Image reconstruction
Integrated circuits
Matching pursuit algorithms
Message passing
Power consumption
Restoration
Scalable video coding (SVC)
Signal processing algorithms
Signal reconstruction
Sparsity
ISSN:1051-8215, 1558-2205
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Zusammenfassung:Compressed sensing (CS) enables the reconstruction of sparse signals from a small number of linear measurements. However, sparse signal recovery algorithms require significant computational effort, even for problems of moderate size, and make their hardware realization a highly challenging task. In this paper, we present a novel architecture for the reconstruction of the sparse signal using the recently proposed Enhanced Approximate Message Passing (EAMP) algorithm. The EAMP algorithm shows better performance in terms of convergence rate and sparsity measurement trade-off as compared to AMP, IST and IHT techniques. The execution time of the proposed design has been reduced by maximizing parallelism with an appropriate level of unfolding. It receives CS measurements (y) and the number of non-zero elements (K) in the input vector from the encoder. Depending on the value of K, one sensing matrix (<inline-formula> <tex-math notation="LaTeX">{\Phi }_{{{j}}} </tex-math></inline-formula>) is selected from the code book <inline-formula> <tex-math notation="LaTeX">\Phi </tex-math></inline-formula>. The reconstruction algorithm (EAMP) recovers the input vector ( s ) by using y and <inline-formula> <tex-math notation="LaTeX">\Phi </tex-math></inline-formula>. Bitonic sorting has been used to identify the thresholding value in the EAMP algorithm. The proposed architecture for EAMP algorithm has been synthesized using Synopsys design compiler with 65 nm technology CMOS standard cell libraries. It occupies 719.15 KGE and consumes 315.5 mW power at a frequency of 400 MHz. It is suitable for any arbitrary sparsity-based signal restoration and CS problems.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2019.2943363