论文标题
传感器噪声中有效的可编程随机变化生成加速器
Efficient Programmable Random Variate Generation Accelerator from Sensor Noise
论文作者
论文摘要
我们引入了一种基于在受控环境中进行物理过程采样的非均匀随机数生成的方法。我们证明了该方法的一种概念验证实施，该方法将单变量高斯单变量的蒙特卡洛整合误差降低了1068倍，同时使蒙特卡洛模拟的速度加倍。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漂移。
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.
