Low-complexity FPGA implementation of compressive sensing reconstruction

TLDR: A novel thresholding method is used to reduce the processing time for the optimization problem by at least 25 % and the proposed architecture reconstructs a 256-length signal with maximum sparsity of 8 and using 64 measurements.

Abstract: Compressive sensing (CS) is a novel technology which allows sampling of sparse signals under sub-Nyquist rate and reconstructing the image using computational intensive algorithms. Reconstruction algorithms are complex and software implementation of these algorithms is extremely slow and power consuming. In this paper, a low complexity architecture for the reconstruction of compressively sampled signals is proposed. The algorithm used here is Orthogonal Matching Pursuit (OMP) which can be divided into two major processes: optimization problem and least square problem. The most complex part of OMP is to solve the least square problem and a scalable Q-R decomposition (QRD) core is implemented to perform this. A novel thresholding method is used to reduce the processing time for the optimization problem by at least 25 %. The proposed architecture reconstructs a 256-length signal with maximum sparsity of 8 and using 64 measurements. Implementation on Xilinx Virtex-5 FPGA runs at two clock rates (85 MHz and 69 MHz), and occupies an area of 15% slices and 80% DSP cores. The total reconstruction for a 128-length signal takes 7.13 μs which is 3.4 times faster than the state-of-art-implementation.