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.
Massive computation of the reconstruction algorithm for compressive sensing (CS) has been a major concern for its real‐time application. In this paper, we propose a novel high‐speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Least‐squares problem solving, which requires a huge amount of computations in the OMP, is implemented by using systolic arrays with four new processing elements. In addition, a distributed‐arithmetic‐based circuit for matrix multiplication is proposed to counterbalance the area overhead caused by the multi‐stage pipelining. The results of logic synthesis show that the proposed design reconstructs signals nearly 19 times faster while occupying an only 1.06 times larger area than the existing designs for N = 256, M = 64, and m = 16, where N is the number of the original samples, M is the length of the measurement vector, and m is the sparsity level of the signal.
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