Word-Length Optimization and Error Analysis of a Multivariate Gaussian Random Number Generator

Provided by: Imperial College London
Topic: Hardware
Format: PDF
Monte carlo simulation is one of the most widely used techniques for computationally intensive simulations in mathematical analysis and modeling. A multivariate gaussian random number generator is one of the main building blocks of such a system. Field Programmable Gate Arrays (FPGAs) are gaining increased popularity as an alternative means to the traditional general purpose processors targeting the acceleration of the computationally expensive random number generator block. This paper presents a novel approach for mapping a multivariate gaussian random number generator onto an FPGA by automatically optimizing the computational path with respect to the resource usage.

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