Nonuniform random numbers are key for many technical applications, and designing efficient hardware implementations of non-uniform random number generators is a very active research field. However, ...most state-of-the-art architectures are either tailored to specific distributions or use up a lot of hardware resources. At ReConFig 2010, we have presented a new design that saves up to 48% of area compared to state-of-the-art inversion-based implementation, usable for arbitrary distributions and precision. In this paper, we introduce a more flexible version together with a refined segmentation scheme that allows to further reduce the approximation error significantly. We provide a free software tool allowing users to implement their own distributions easily, and we have tested our random number generator thoroughly by statistic analysis and two application tests.
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.
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