Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.   present a novel approach to create RNs for different distributions based on an efficient transformation of floating-point inputs. For the Gaussian distribution  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 $8.37\sigma$ and achieves 381MHz.  also present a comprehensive testing methodology based on stochastic analysis and verification in practical applications.
['Christian de Schryver', 'Daniel Schmidt', 'Norbert Wehn', 'Elke Korn', 'Henning Marxen', 'Ralf Korn']
I am writing a research paper on a idea A New Hardware Efficient Inversion Based Random Number Generator for Non-uniform Distributions
[{"from": "human", "value": "I am writing a research paper on a idea A New Hardware Efficient Inversion Based Random Number Generator for Non-uniform Distributions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.   present a novel approach to create RNs for different distributions based on an efficient transformation of floating-point inputs. For the Gaussian distribution  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 $8.37\\sigma$ and achieves 381MHz.  also present a comprehensive testing methodology based on stochastic analysis and verification in practical applications."}]