An Efficient FPGA Implementation of Orthogonal Matching Pursuit With Square-Root-Free QR Decomposition
An Efficient FPGA Implementation of Orthogonal Matching Pursuit With Square-Root-Free QR Decomposition
复制标题
无平方根 QR 分解正交匹配追踪的高效 FPGA 实现
DOI：
10.1109/tvlsi.2018.2879884
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Zhou Dian
中科院分区：
文献类型：
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作者：
Ge Xiang;Yang Fan;Zhu Hengliang;Zeng Xuan;Zhou Dian
压缩感知（CS）是一种新的信号处理技术，可以在亚奈奎斯特速率下重构稀疏信号。正交匹配追踪（OMP）是应用最广泛的信号重构算法之一。然而，OMP算法中的最小二乘问题（LSP）限制了其性能。提出了一种基于OMP的CS快速重建算法。该算法采用增量QR分解（QRD）的方法来有效地解决LSP。增量QRD进一步优化，以消除平方根操作，以方便硬件实现。所提出的架构避免了复杂的平方根单元，主要由一些更基本的计算单元，其中的计算过程被分解成几个简单的操作映射到相应的硬件流水线。基于Xilinx Kintex-7 FPGA的拟议实现利用了并行性，通过精心规划的工作负载计划，并达到延迟和频率之间的最佳权衡。实验结果表明，该结构可以在210 MHz的频率下运行，对于36稀疏1024长度的信号，重构时间为238 $\mu \text{s}$，与现有技术相比，信号重构速度提高了1. 43\times $.
Compressive sensing (CS) is a novel signal processing technology to reconstruct the sparse signal at sub-Nyquist rate. Orthogonal matching pursuit (OMP) is one of the most widely used signal reconstruction algorithms. However, the least square problem (LSP) in OMP algorithm limits its performance. This paper presents a fast CS reconstruction algorithm implemented on field-programmable gate array (FPGA) using OMP. The proposed algorithm adopts an incremental QR decomposition (QRD) method to efficiently solve the LSP. The incremental QRD is further optimized to eliminate the square root operation to facilitate hardware implementation. The proposed architecture avoiding the complex square root unit mainly consists of some more basic computing units, where the computing process is broken down into several simple operations to map to the corresponding hardware for pipelining. The proposed implementation based on Xilinx Kintex-7 FPGA exploits the parallelism by a well-planned workload schedule and reaches an optimal tradeoff between the latency and frequency. The experimental results demonstrate that the proposed architecture can run at a frequency of 210 MHz with a reconstruction time of 238 $\mu \text{s}$ for 36-sparse 1024-length signal, which improves the signal reconstruction speed by $1.43\times $ compared to the state-of-the-art implementations.