Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

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 doubles the speed of Monte Carlo integration of a univariate Gaussian. We show that we must measure …

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Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

We introduce a method for non-uniform random number generation based onsampling a physical process in a controlled environment. We demonstrate oneproof-of-concept implementation of the method that reduces the error of MonteCarlo integration of a univariate Gaussian by 1068 times while doubling th…

arXiv.orgJames Timothy Meech

Abstract

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 doubles the speed of Monte Carlo integration of a univariate Gaussian. We show that we must measure and compensate for the supply voltage and temperature of the physical process to prevent the mean and standard deviation from drifting. The method we present and our detailed empirical hardware measurements demonstrate the feasibility of programmable non-uniform random variate generation from low-power sensors and the effect of ADC quantization on the statistical qualities of the approach.

Cite as:

J. T. Meech and P. Stanley-Marbell, "Efficient Programmable Random Variate Generation Accelerator From Sensor Noise," in IEEE Embedded Systems Letters, vol. 13, no. 3, pp. 73-76, Sept. 2021, doi: 10.1109/LES.2020.3007005.

Bibtex:

@article{9133181,
 author={Meech, James T. and Stanley-Marbell, Phillip},
 journal={IEEE Embedded Systems Letters}, 
 title={Efficient Programmable Random Variate Generation Accelerator From Sensor Noise}, 
 year={2021},
 volume={13},
 number={3},
 pages={73-76},
 doi={10.1109/LES.2020.3007005}
}