IRCA-TRNG: A Lightweight Dual-Ring Chaotic TRNG With Perturbation Refresh for High Throughput
As a core component in the field of information security, the true random number generator (TRNG) produces high-entropy random numbers by extracting unpredictable noise from the physical environment, exhibiting nonreproducibility and resistance to prediction. To address the challenges posed by interference in high-speed systems, maintaining stable throughput and entropy sources for TRNG, this article proposes an optimized TRNG that utilizes chaotic interference to refresh the cellular automata (IRCA) iterative algorithm. The IRCA-TRNG utilizes a self-timed ring oscillator (STR) and jitter to perturb the operation of chaotic cellular automata (CA) cells, achieving a high-throughput TRNG. The generated random sequences have successfully passed NIST SP800-22, TESTU01, NIST SP800-90B, and AIS-31 tests. A throughput of 1040 Mb/s has been achieved on Xilinx Artix-7 and PYNQ-K2 series development boards. Compared with the state-of-the-art works, the proposed TRNG demonstrates significant advantages in resource utilization and performance quality factors.
Authors
- Yaohua Xu (ORCID: https://orcid.org/0000-0003-0861-1802)
- Peiyang Kang
- Yunlai Zhu (ORCID: https://orcid.org/0000-0003-2546-5739)
- Liang Yao (ORCID: https://orcid.org/0000-0002-4081-6499)
- Yingchun Lu (ORCID: https://orcid.org/0000-0002-2621-0933)
- Aibin Yan (ORCID: https://orcid.org/0000-0003-0024-987X)
- Zhengfeng Huang (ORCID: https://orcid.org/0000-0001-8695-4478)
- Ying Zhang (ORCID: https://orcid.org/0000-0002-2944-5420)
- Huaguo Liang (ORCID: https://orcid.org/0000-0002-0307-7236)
- Danfei Shi
Institutions
- Anhui University (CN)
- Hefei University of Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Very Large Scale Integration (VLSI) Systems
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1109/tvlsi.2025.3608292
- Citations
- 3
- Primary Topic
- Cellular Automata and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.51