survey

Gaussian Random Number Generation: A Survey on Hardware Architectures

Authors:

Jamshaid Sarwar Malik,

Ahmed HemaniAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 49, Issue 3

Article No.: 53, Pages 1 - 37

https://doi.org/10.1145/2980052

Published: 01 November 2016 Publication History

Abstract

Some excellent surveys of the Gaussian random number generators (GRNGs) from the algorithmic perspective exist in the published literature to date (e.g., Thomas et al. [2007]). In the last decade, however, advancements in digital hardware have resulted in an ever-decreasing hardware cost and increased design flexibility. Additionally, recent advances in applications like gaming, weather forecasting, and simulations in physics and astronomy require faster, cheaper, and statistically accurate GRNGs. These two trends have contributed toward the development of a number of novel GRNG architectures optimized for hardware design. A detailed comparative study of these hardware architectures has been somewhat missing in the published literature. This work provides the potential user a capsulization of the published hardware GRNG architectures. We have provided the method and theory, pros and cons, and a comparative summary of the speed, statistical accuracy, and hardware resource utilization of these architectures. Finally, we have complemented this work by describing two novel hardware GRNG architectures, namely, the CLT-inversion and the multihat algorithm, respectively. These new architectures provide high tail accuracy (6σ and 8σ, respectively) at a low hardware cost.

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Index Terms

Gaussian Random Number Generation: A Survey on Hardware Architectures
1. Hardware
  1. Integrated circuits
    1. Logic circuits
      1. Arithmetic and datapath circuits
2. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 49, Issue 3

September 2017

658 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2988524

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering/University of Florida/Gainesville, FL

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Copyright © 2016 ACM.

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Publication History

Published: 01 November 2016

Accepted: 01 July 2016

Revised: 01 May 2016

Received: 01 June 2015

Published in CSUR Volume 49, Issue 3

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https://doi.org/10.1007/978-3-031-81824-0_8
Broneske DBurtsev VDrewes AGurumurthy BPionteck TSaake G(2025)ADAMANT: Hardware-Accelerated Query Processing Made EasyScalable Data Management for Future Hardware10.1007/978-3-031-74097-8_1(1-38)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-74097-8_1
Jia XGu HLiu YYang JWang XPan WZhang YCotofana SZhao W(2024)An Energy-Efficient Bayesian Neural Network Implementation Using Stochastic Computing MethodIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.326553335:9(12913-12923)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3265533
Chen ZSong KZou DZhu CXu Y(2024)Flexible FPGA Gaussian Random Number Generators With Reconfigurable VarianceIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.337473171:5(2384-2397)Online publication date: May-2024
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