A parallel and reconfigurable architecture for efficient OMP compressive sensing reconstruction

Abstract

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 reconstruction techniques are computationally intensive and power consuming, which make them impractical for embedded applications. This work presents a parallel and reconfigurable architecture for Orthogonal Matching Pursuit (OMP) algorithm, one of the most popular CS reconstruction 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 parallelization 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 architecture can reconstruct images of different sizes and measurements and is implemented on a Xilinx Virtex 7 FPGA. The results indicate that, for a 128x128 image reconstruction, the proposed reconfigurable architecture is 2.67x to 1.8x faster than the previous non-reconfigurable work which is less complex and uses much smaller sparsity.