Mapping Multiple Multivariate Gaussian Random Number Generators on an FPGA

A Multivariate Gaussian random number generator (MVGRNG) is an essential block for many hardware designs, including Monte Carlo simulations. These simulations are usually used in applications such as statistical physics and financial mathematics. Field Programmable Gate Arrays (FPGAs) are often used to implement these generators as the design can be effectively optimized. Many applications require random samples from a number of multivariate Gaussian distributions leading to a problem of efficiently mapping of the required MVGRNG on an FPGA. The proposed approach presented in this paper exploits any redundancy that exists between different distributions under consideration leading to designs with improved resource usage. Experimental results demonstrate that the proposed approach outperforms the existing approaches by producing MVGRNG designs that utilize less hardware resources in comparison to existing approaches achieving up to 50% reduction of hardware resource utilization.