  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.

Other Computer Science;Signal Processing;Machine Learning