Hardware acceleration of pseudo-random number generation for simulation applications

Abstract

In modeling and simulation tools, random numbers from a variety of probability distribution functions are generated to simulate the behavior of random events. Inefficient generation of these numbers can be a significant bottleneck for simulation applications. Generating these random numbers imprecisely can skew results. An efficient and scalable fixed-point method for generating random numbers for any probability distribution function in a Field Programmable Gate Array (FPGA) is developed. A Pi estimator, a Monte Carlo integrator, and a stochastic simulator for chemical species are developed in software. Estimates are made regarding their potential to be accelerated using the designed FPGA. Results are presented which examine trade-offs between the number of gates used by the FPGA and the accuracy of the random numbers generated. The work shows that generating random numbers using the designed hardware can significantly increase the performance of simulation applications that require many random numbers.