Abstract
Compressed Sensing (CS) is found to be promising method for sparse signal recovery and sampling. The paper proposes the architecture for computing various computational functions useful in realizing CS recovery consisting of Singular Value Decomposition (SVD) using Bi-diagonalization method; L1 norm of vector, L2 norm of vector calculations. This is one of the early VLSI implementation attempt for CS recovery. We have verified the design for speed and accuracy of results on FPGA.
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Korde, S., Khandare, A., Deshmukh, R., Patrikar, R. (2013). Computational Functions’ VLSI Implementation for Compressed Sensing. In: Gaur, M.S., Zwolinski, M., Laxmi, V., Boolchandani, D., Sing, V., Sing, A.D. (eds) VLSI Design and Test. Communications in Computer and Information Science, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42024-5_5
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DOI: https://doi.org/10.1007/978-3-642-42024-5_5
Publisher Name: Springer, Berlin, Heidelberg
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