基于FPGA的高通量Cholesky分解

Jun Luo, Qijun Huang, Sheng Chang, Xiaoying Song, Yun Shang
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引用次数: 8

摘要

乔列斯基分解在解决许多工程和科学问题方面有着广泛的应用。在这些问题中,加速是一个重要的问题。针对基于最小平方误差准则的维纳滤波问题,提出了一种基于硬件的高吞吐量LLT Cholesky分解方法。为了达到最佳的效率,硬件实现采用了定点多结构和不同的流水线阶段。利用并行特性来提高吞吐量。结果表明,与基于软件的方法相比,实现了显着的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High throughput Cholesky decomposition based on FPGA
Cholesky decomposition has wide applications in solving many engineering and scientific problems. Acceleration is an important issue in many of these problems. In this paper, a hardware-based LLT Cholesky decomposition featuring high throughput has been presented to solve wiener filtering based on the minimum square error criterion. To achieve the best efficiency, the hardware-based implementation has been realized by fixed-point multiple structures and various pipeline stages. Parallel properties have been exploited to improve the throughput. Results have shown that a significant speedup has been achieved compared to the software-based approach.
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