High-performance ASIC realization of orthogonal matching pursuit algorithm

Abstract

Compressive sensing (CS) is perceived as a breakthrough in sampling theory, as it proves that sparse signals can be reconstructed from fewer samples than required by the Shannon-Nyquist theorem. However, CS can hardly be applied to real-time applications because reconstruction algorithms are computationally demanding. To tackle this problem, in this paper, we propose a new high-speed architecture for implementing the orthogonal matching pursuit (OMP), which is one of the most popular algorithms for CS reconstruction. Specifically, a novel pipelined systolic architecture and an optimized scheduling strategy are proposed. From the synthesis results, we find that the proposed design takes 1.638 µs to reconstruct 16-sparse signal, which is 19.2 times faster than the existing VLSI implementation of the OMP.

Covert Anti-Jamming Communication Based on Gaussian Coded Modulation

“…ASICs can be used to further increase the throughput. A 65-nm complementary metal-oxide-semiconductor ASIC design [48] can process the GSJE algorithm for N = 256 within 591.36 ns. By assuming N = 256 and L = 8, a throughput of 13.53 Mbps can be achieved.…”

Painting Psychotherapy Image Measurement and Reconstruction Algorithm based on Improved Hadamard Matrix

“…In this study, the algorithm of painting psychotherapy image measurement and reconstruction based on the improved Hadamard matrix was designed using the principle of the Orthogonal Matching Pursuit (OMP) algorithm (Nguyen and Park, 2018). The idea of the medical image reconstruction algorithm was as follows:…”