High throughput Cholesky decomposition based on FPGA

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

Abstract

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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基于FPGA的高通量Cholesky分解
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。
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