Efficient Programmable Random Variate Generation Accelerator from Sensor Noise
This addresses the need for efficient random variate generation in applications like Monte Carlo simulations, though it appears incremental as it builds on existing physical noise methods with added control mechanisms.
The paper tackles the problem of non-uniform random number generation by sampling a physical process in a controlled environment, resulting in a 1068 times reduction in error for Monte Carlo integration of a univariate Gaussian while doubling simulation speed.
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.