A Parallel and Reconfigurable Architecture for Efficient OMP Compressive Sensing Reconstruction

Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. However, the signal re-construction techniques are computationally intensive and power consuming, which make them impractical for em-bedded applications. This work presents a parallel and re-configurable architecture for Orthogonal Matching Pursuit (OMP) algorithm, one of the most popular CS reconstruc-tion algorithms. In this paper, we are proposing the first reconfigurable OMP CS reconstruction architecture which can take different image sizes with sparsity up to 32. The aim is to minimize the hardware complexity, area and power consumption, and improve the reconstruction latency while meeting the reconstruction accuracy. First, the accuracy of reconstructed images is analyzed for different sparsity values and fixed point word length reduction. Next, efficient paral-lelization techniques are applied to reconstruct signals with variant signal lengths of N. The OMP algorithm is mainly divided into three kernels, where each kernel is parallelized to reduce execution time, and efficient reuse of the matrix operators allows us to reduce area. The proposed architec-ture can reconstruct images of different sizes and measure-ments and is implemented on a Xilinx Virtex 7 FPGA. The results indicate that, for a 128×128 image reconstruction, the proposed reconfigurable architecture is 2.67 × to 1.8× faster than the previous non-reconfigurable work which is less complex and uses much smaller sparsity