A hardware efficient random number generator for nonuniform distributions with arbitrary precision

Authors:
Christian De Schryver;Daniel Schmidt;Norbert Wehn;Elke Korn;Henning Marxen;Anton Kostiuk;Ralf Korn
Affiliations:
Microelectronic Systems Design Research Group, University of Kaiserslautern, Kaiserslautern, Germany;Microelectronic Systems Design Research Group, University of Kaiserslautern, Kaiserslautern, Germany;Microelectronic Systems Design Research Group, University of Kaiserslautern, Kaiserslautern, Germany;Stochastic Control and Financial Mathematics Group, University of Kaiserslautern, Kaiserslautern, Germany;Stochastic Control and Financial Mathematics Group, University of Kaiserslautern, Kaiserslautern, Germany;Stochastic Control and Financial Mathematics Group, University of Kaiserslautern, Kaiserslautern, Germany;Stochastic Control and Financial Mathematics Group, University of Kaiserslautern, Kaiserslautern, Germany
Venue:
International Journal of Reconfigurable Computing - Special issue on Selected Papers from the International Conference on Reconfigurable Computing and FPGAs (ReConFig'10)
Year:
2012

Citing 13
Cited 1

Random number generation and quasi-Monte Carlo methods

Random number generation and quasi-Monte Carlo methods
Fast pseudorandom generators for normal and exponential variates

ACM Transactions on Mathematical Software (TOMS)
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms

The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
A Hardware Gaussian Noise Generator Using the Box-Muller Method and Its Error Analysis

IEEE Transactions on Computers
TestU01: A C library for empirical testing of random number generators

ACM Transactions on Mathematical Software (TOMS)
Gaussian random number generators

ACM Computing Surveys (CSUR)
FPGA Implementation of Pseudo Random Number Generators for Monte Carlo Methods in Quantitative Finance

RECONFIG '08 Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs
Hardware generation of arbitrary random number distributions from uniform distributions via the inversion method

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Mersenne Twister Random Number Generation on FPGA, CPU and GPU

AHS '09 Proceedings of the 2009 NASA/ESA Conference on Adaptive Hardware and Systems
A New Hardware Efficient Inversion Based Random Number Generator for Non-uniform Distributions

RECONFIG '10 Proceedings of the 2010 International Conference on Reconfigurable Computing and FPGAs
A hardware gaussian noise generator using the wallace method

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Hierarchical segmentation for hardware function evaluation

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

A multi-level Monte Carlo FPGA accelerator for option pricing in the Heston model

Proceedings of the Conference on Design, Automation and Test in Europe

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nonuniform random numbers are key for many technical applications, and designing efficient hardware implementations of nonuniform 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.