Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

--We introduce a method for nonuniform 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 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. URRENT software-based methods of nonuniform random variate generation are slow and inefficient [1][2][3][4]. We present a programmable system capable of generating Gaussian random variates by extracting the noise properties of a MEMS sensor.