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Fast Implementation for the Singular Value and Eigenvalue Decomposition Based on FPGA

机译:基于FPGA的奇异值和特征值分解的快速实现。

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摘要

A fast and efficient hardware implementation for computing the Singular value decomposition (SVD) and Eigenvalue decomposition (EVD) is presented. Considering that the SVD and EVD are complex and expensive operations, to achieve high performance with low computing complexity, our approach takes full advantage of the combination of parallel and sequential computation, which can increase efficiently the hardware utilization. Besides, regarding to EVD, we propose a hardware solution of a simplified Coordinate rotation digital computer (CORDIC)-like algorithm which can obtain higher speed. The performance analysis and comparison results show that the proposed methods can be realized on Filed-programmable gate arrays (FPGAs) with less computation time by using systolic array. It will be shown that the proposed implementation could be an efficient alternative for real-time applications.
机译:提出了一种用于计算奇异值分解(SVD)和特征值分解(EVD)的快速有效的硬件实现。考虑到SVD和EVD是复杂且昂贵的操作,为了以较低的计算复杂度实现高性能,我们的方法充分利用了并行计算和顺序计算相结合的优势,可以有效地提高硬件利用率。此外,针对EVD,我们提出了一种简化的类似坐标旋转数字计算机(CORDIC)的算法的硬件解决方案,该算法可以实现更高的速度。性能分析和比较结果表明,采用脉动阵列可以在较少的计算时间的情况下在现场可编程门阵列(FPGA)上实现。将表明,所提出的实施方式可以是实时应用的有效替代方案。

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