A FPGA Based Parallel Multivariate Gaussian Random Number Generation Framework

Multivariate Gaussian distribution random numbers are widely used in the field of machine learning and financial engineering computation. Based on the Fast Jump Ahead algorithm, a parallel hardware acceleration architecture for generating multivariable Gaussian vectors is proposed in this paper. The structure uses WELL19937 algorithm as the basic uniformly distributed random number generator and is capable of generating arbitrary number of parallel uncorrelated multivariate Gaussian vector sequences. The experimental results show that the parallel acceleration structure with P parallelism can achieve the throughput of generating P / N random vectors per cycle for dimension N, which is superior to the related work and the implementation of CPU and GPU.