Fast pseudorandom generators for normal and exponential variates
ACM Transactions on Mathematical Software (TOMS)
Variance reduction techniques for value-at-risk with heavy-tailed risk factors
Proceedings of the 32nd conference on Winter simulation
Synthesis And Optimization Of DSP Algorithms
Synthesis And Optimization Of DSP Algorithms
A Hardware Gaussian Noise Generator Using the Box-Muller Method and Its Error Analysis
IEEE Transactions on Computers
Gaussian random number generators
ACM Computing Surveys (CSUR)
Multivariate Gaussian Random Number Generation Targeting Reconfigurable Hardware
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Multivariate Gaussian Random Number Generator Targeting Specific Resource Utilization in an FPGA
ARC '08 Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications
Synthesis and Optimization of 2D Filter Designs for Heterogeneous FPGAs
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Credit Risk Modelling using Hardware Accelerated Monte-Carlo Simulation
FCCM '08 Proceedings of the 2008 16th International Symposium on Field-Programmable Custom Computing Machines
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
Word-Length Optimization and Error Analysis of a Multivariate Gaussian Random Number Generator
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
A hardware gaussian noise generator using the wallace method
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Design of a financial application driven multivariate gaussian random number generator for an FPGA
ARC'10 Proceedings of the 6th international conference on Reconfigurable Computing: architectures, Tools and Applications
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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 article presents a novel approach for mapping a multivariate Gaussian random number generator onto an FPGA by optimizing the computational path in terms of hardware resource usage subject to an acceptable error in the approximation of the distribution of interest. The proposed approach is based on the eigenvalue decomposition algorithm which leads to a design with different precision requirements in the computational paths. An analysis on the impact of the error due to truncation/rounding operation along the computational path is performed and an analytical expression of the error inserted into the system is presented. Based on the error analysis, three algorithms that optimize the resource utilization and at the same time minimize the error in the output of the system are presented and compared. Experimental results reveal that the hardware resource usage on an FPGA as well as the error in the approximation of the distribution of interest are significantly reduced by the use of the optimization techniques introduced in the proposed approach.