A New Hardware Efficient Inversion Based Random Number Generator for Non-uniform Distributions

For numerous computationally complex applications, like financial modelling and Monte Carlo simulations, the fast generation of high quality non-uniform random numbers (RNs) is essential. The implementation of such generators in FPGA-based accelerators has therefore become a very active research field. In this paper we present a novel approach to create RNs for different distributions based on an efficient transformation of floating-point inputs. For the Gaussian distribution we can reduce the number of slices needed by up to 48% compared to the state-of-the-art while achieving a higher output precision in the tail region. Our architecture produces samples up to 8.37σ and achieves 381MHz. We also present a comprehensive testing methodology based on stochastic analysis and verification in practical applications.