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.

中文翻译：

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压缩匹配重构中正交匹配跟踪算法的高通量和低面积实现

压缩感测（CS）重建算法的大规模计算一直是其实时应用的主要关注点。在本文中，我们为正交匹配追踪（OMP）算法提出了一种新颖的高速架构，这是最常用于重建压缩感测信号的算法。所提出的设计提供了非常高的吞吐量，并包括创新的管线架构和调度算法。通过使用带有四个新处理元素的脉动数组来实现最小二乘问题求解，这需要在OMP中进行大量计算。此外，提出了一种用于矩阵乘法的基于分布式算术的电路，以平衡由多级流水线引起的面积开销。N  = 256，M  = 64，m  = 16，其中N是原始样本的数量，M是测量矢量的长度，m是信号的稀疏度。