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Design of Hardware Gausian Random Number Generator Considering Side-Effect

机译:考虑副作用的硬件高斯随机数发生器设计

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research on hardware accelerator has drawn more and more attention nowadays. Many compute-intensive algorithm could be accelerated here, such as Monte Carlo simulation and image processing. Gaussian Random Number Generators (GRNG) are always at the core of these algorithms. In this paper, we analysize the Side-Effect on the design of Box Muller GRNG and draw the relation between the output range of the generated Gaussian random numbers and the bit-width L of the uniform random numbers. To prove the relation drawn here, we design three different GRNGs with L=8, L=16 and L=32. The implementation results show that our analysis conclusions just match the facts.
机译:如今,关于硬件加速器的研究越来越受到关注。这里可以加速许多计算密集型算法,例如蒙特卡洛模拟和图像处理。高斯随机数发生器(GRNG)始终是这些算法的核心。在本文中,我们分析了Box Muller GRNG设计的副作用,并绘制了生成的高斯随机数的输出范围与统一随机数的位宽L之间的关系。为了证明这里绘制的关系,我们设计了三种不同的GRNG,分别为L = 8,L = 16和L = 32。实施结果表明,我们的分析结论与事实相符。

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