We introduce a method for non-uniform random number generation based on sampling a physical process in a controlled environment. We demonstrate one proof-of-concept implementation of the method that reduces the error of Monte Carlo integration of a univariate Gaussian by 1068 times while doubling the speed of the Monte Carlo simulation. We show that the supply voltage and temperature of the physical process must be controlled to prevent the mean and standard deviation of the random number generator from drifting.

中文翻译：

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来自传感器噪声的高效可编程随机变量生成加速器

我们介绍了一种基于在受控环境中对物理过程进行采样的非均匀随机数生成方法。我们演示了该方法的一个概念验证实现，该方法将单变量高斯的蒙特卡罗积分误差减少了 1068 倍，同时将蒙特卡罗模拟的速度提高了一倍。我们表明，必须控制物理过程的电源电压和温度，以防止随机数发生器的平均值和标准偏差发生漂移。