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A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Junxiu Liu, Zhewei Liang, Yuling Luo, Lvchen Cao, Shunsheng Zhang, Yanhu Wang, Scott Yang Orcid Logo

Micromachines, Volume: 12, Issue: 1, Start page: 31

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.3390/mi12010031

Abstract

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, wh...

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Published in: Micromachines
ISSN: 2072-666X
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa58944
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first_indexed 2021-12-07T09:55:20Z
last_indexed 2021-12-31T04:28:59Z
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spelling 2021-12-30T13:35:58.7342760 v2 58944 2021-12-07 A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2021-12-07 SCS Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches. Journal Article Micromachines 12 1 31 MDPI AG 2072-666X stochastic computing; chaos; logistic map; FPGA 30 12 2020 2020-12-30 10.3390/mi12010031 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This research was partially supported by the National Natural Science Foundation of China under Grants 61661008 and 61801131, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Diecai Project of Guangxi Normal University, 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program, and research fund of Guangxi Key Lab of Multi-source Information Mining & Security (19-A-03-02) 2021-12-30T13:35:58.7342760 2021-12-07T09:55:04.4818997 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Junxiu Liu 1 Zhewei Liang 2 Yuling Luo 3 Lvchen Cao 4 Shunsheng Zhang 5 Yanhu Wang 6 Scott Yang 0000-0002-6618-7483 7 58944__21966__3a768795221e485fb5ff4a193382d432.pdf 58944.pdf 2021-12-30T13:34:40.5442409 Output 1780763 application/pdf Version of Record true © 2020 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
spellingShingle A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
Scott Yang
title_short A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
title_full A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
title_fullStr A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
title_full_unstemmed A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
title_sort A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Junxiu Liu
Zhewei Liang
Yuling Luo
Lvchen Cao
Shunsheng Zhang
Yanhu Wang
Scott Yang
format Journal article
container_title Micromachines
container_volume 12
container_issue 1
container_start_page 31
publishDate 2020
institution Swansea University
issn 2072-666X
doi_str_mv 10.3390/mi12010031
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.
published_date 2020-12-30T04:15:52Z
_version_ 1763754065256251392
score 11.037581

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