skip to main content
10.1145/3386263.3406922acmotherconferencesArticle/Chapter ViewAbstractPublication PagesglsvlsiConference Proceedingsconference-collections
research-article

Towards Programmable All-Digital True Random Number Generator

Published: 07 September 2020 Publication History

Abstract

Random number generator (RNG) is a core component in many applications such as scientific research, testing and diagnosis, gaming, and cryptosystems (e.g., obfuscation, encryption, and authentication). Although, there are various RNG designs targeting specific application goals such as low-power, high-throughput, stronger security guarantees, a universal programmable RNG design has remained elusive. Indeed, it is a challenge to have only one RNG unit in a system with multiple compute modules with different randomness requirements. In this work, we aim to provide a practical solution to this design challenge by proposing a multi-purpose true random number generator (TRNG), which can be configured in real time to generate random sequences with different requirements. Such a programmable TRNG is able to supply random bits to multiple modules with different demands. The proposed TRNG is a highly convenient multi-purpose hardware primitive that can be deployed in many designs as it provides a tunable physical entropy source and a dynamic cost-performance trade-off.

Supplementary Material

MP4 File (3386263.3406922.mp4)
Presentation video

References

[1]
A Akhshani, A Akhavan, A Mobaraki, S-C Lim, and Z Hassan. Pseudo random number generator based on quantum chaotic map. Communications in Nonlinear Science and Numerical Simulation, Vol. 19, 1 (2014).
[2]
Luis Gerardo de la Fraga, Esteban Torres-Pérez, Esteban Tlelo-Cuautle, and Cuauhtemoc Mancillas-López. Hardware implementation of pseudo-random number generators based on chaotic maps. Nonlinear Dynamics, Vol. 90, 3 (2017).
[3]
Michael Francc ois, Thomas Grosges, Dominique Barchiesi, and Robert Erra. Pseudo-random number generator based on mixing of three chaotic maps. Communications in Nonlinear Science and Numerical Simulation, Vol. 19, 4 (2014).
[4]
Minseo Kim, Unsoo Ha, Kyuho Jason Lee, Yongsu Lee, and Hoi-Jun Yoo. A 82-nw chaotic map true random number generator based on a sub-ranging sar adc. IEEE Journal of Solid-State Circuits, Vol. 52, 7 (2017), 1953--1965.
[5]
Volodymyr Lynnyk, Noboru Sakamoto, and Sergej Celikovskỳ. Pseudo random number generator based on the generalized Lorenz chaotic system. IFAC-PapersOnLine (2015).
[6]
Fatih Özkaynak. Cryptographically secure random number generator with chaotic additional input. Nonlinear Dynamics, Vol. 78, 3 (2014), 2015--2020.
[7]
Yong Wang, Zhaolong Liu, Jianbin Ma, and Haiyuan He. A pseudorandom number generator based on piecewise logistic map. Nonlinear Dynamics, Vol. 83, 4 (2016), 2373--2391.
[8]
Octavian Cret, Alin Suciu, and Tamas Gyorfi. Practical issues in implementing trngs in fpgas based on the ring oscillator sampling method. In Symbolic and Numeric Algorithms for Scientific Computing, 2008. SYNASC'08. 10th International Symposium on. IEEE, 433--438.
[9]
J-L Danger, Sylvain Guilley, and Philippe Hoogvorst. High speed true random number generator based on open loop structures in FPGAs. Microelectronics journal, Vol. 40, 11 (2009), 1650--1656.
[10]
Paul Kohlbrenner and Kris Gaj. An embedded true random number generator for FPGAs. In Proceedings of the 2004 ACM/SIGDA 12th international symposium on Field programmable gate arrays. ACM, 71--78.
[11]
Sammy HM Kwok and Edmund Y Lam. FPGA-based high-speed true random number generator for cryptographic applications. In TENCON 2006. 2006 IEEE Region 10 Conference. IEEE, 1--4.
[12]
Honorio Martin, Pedro Martin-Holgado, Pedro Peris-Lopez, Yolanda Morilla, and Luis Entrena. On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation. Entropy, Vol. 20, 7 (2018), 513.
[13]
K. H. Tsoi, KH Leung, and Philip Heng Wai Leong. Compact FPGA-based true and pseudo random number generators. In Field-Programmable Custom Computing Machines, 2003. FCCM 2003. 11th Annual IEEE Symposium on. IEEE, 51--61.
[14]
Piotr Zbigniew Wieczorek and Krzysztof Golofit. Dual-metastability time-competitive true random number generator. IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 61, 1 (2014), 134--145.
[15]
R. Agrawal, L. Bu, E. D. Rosario, and M. A. Kinsy. Design-flow Methodology for Secure Group Anonymous Authentication. In 2020 Design, Automation Test in Europe Conference Exhibition (DATE). 1544--1549.
[16]
Elaine Barker and Allen Roginsky. Transitions: Recommendation for transitioning the use of cryptographic algorithms and key lengths. NIST Special Publication (2011).
[17]
Lawrence E Bassham III, Andrew L Rukhin, Juan Soto, James R Nechvatal, Miles E Smid, Elaine B Barker, Stefan D Leigh, Mark Levenson, Mark Vangel, David L Banks, et al. Sp 800--22 rev. 1a. a statistical test suite for random and pseudorandom number generators for cryptographic applications. (2010).
[18]
L Bu, H Cheng, and M. A. Kinsy. Fast Dynamic Device Authentication Based on Lorenz Chaotic Systems. IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (2018).
[19]
Lake Bu, Mihailo Isakov, and Michel A. Kinsy. A secure and robust scheme for sharing confidential information in IoT systems. Ad Hoc Networks, Vol. 92 (Sep 2019), 101762.
[20]
Zhongwei Cao, Fangyue Chen, Bo Chen, and Xia Zhang. Research on the Balanced Boolean Functions Satisfying Strict Avalanche Criterion. In 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 680--684.
[21]
M. A. Kinsy, S. Khadka, M. Isakov, and A. Farrukh. Hermes: Secure heterogeneous multicore architecture design. In 2017 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). 14--20.
[22]
A Yu Pogromsky, G Santoboni, and H Nijmeijer. An ultimate bound on the trajectories of the Lorenz system and its applications. Nonlinearity (2003).
[23]
André Seznec and Nicolas Sendrier. HAVEGE: A user-level software heuristic for generating empirically strong random numbers. ACM Transactions on Modeling and Computer Simulation (TOMACS), Vol. 13, 4 (2003), 334--346.
[24]
Zhen Wang, Mark G. Karpovsky, and Konrad Kulikowski. Replacing linear hamming codes by robust nonlinear codes results in a reliability improvement of memories. (2009).

Cited By

View all
  • (2023)Self-Parameterized Chaotic Map for Low-Cost Robust ChaosJournal of Low Power Electronics and Applications10.3390/jlpea1301001813:1(18)Online publication date: 13-Feb-2023
  • (2023)Robust Chaos With Novel 4-Transistor MapsIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2022.321741670:3(914-918)Online publication date: Mar-2023
  • (2023)Normalized Linearly-Combined Chaotic System: Design, Analysis, Implementation, and ApplicationIEEE Open Journal of the Industrial Electronics Society10.1109/OJIES.2023.33284974(486-505)Online publication date: 2023
  • Show More Cited By

Index Terms

  1. Towards Programmable All-Digital True Random Number Generator

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI
    September 2020
    597 pages
    ISBN:9781450379441
    DOI:10.1145/3386263
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 September 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Badges

    • Best Paper
    • Honorable Mention

    Author Tags

    1. FPGA
    2. Lorenz systems
    3. chaotic maps
    4. true random number generation

    Qualifiers

    • Research-article

    Conference

    GLSVLSI '20
    GLSVLSI '20: Great Lakes Symposium on VLSI 2020
    September 7 - 9, 2020
    Virtual Event, China

    Acceptance Rates

    Overall Acceptance Rate 312 of 1,156 submissions, 27%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)19
    • Downloads (Last 6 weeks)3
    Reflects downloads up to 10 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Self-Parameterized Chaotic Map for Low-Cost Robust ChaosJournal of Low Power Electronics and Applications10.3390/jlpea1301001813:1(18)Online publication date: 13-Feb-2023
    • (2023)Robust Chaos With Novel 4-Transistor MapsIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2022.321741670:3(914-918)Online publication date: Mar-2023
    • (2023)Normalized Linearly-Combined Chaotic System: Design, Analysis, Implementation, and ApplicationIEEE Open Journal of the Industrial Electronics Society10.1109/OJIES.2023.33284974(486-505)Online publication date: 2023
    • (2023)Split-Slope Chaotic Map Providing High Entropy Across Wide Range2023 24th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED57927.2023.10129295(1-6)Online publication date: 5-Apr-2023
    • (2022)A Low-Complexity Method to Address Process Variability in True Random Number Generators based on Digital Nonlinear Oscillators2022 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS48785.2022.9937869(1670-1674)Online publication date: 28-May-2022
    • (2022)Cascading CMOS-Based Chaotic Maps for Improved Performance and Its Application in Efficient RNG DesignIEEE Access10.1109/ACCESS.2022.316280610(33758-33770)Online publication date: 2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media