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Optimization of the PLL Based TRNG Design Using the Genetic Algorithm.

Abstract : Phase-locked loop (PLL) based true random number generator (TRNG) is very well suited for security applications using field programmable gate arrays (FPGAs) because most of FPGAs feature hardwired PLL blocks. PLL based TRNGs (PLL-TRNGs) are easy to implement and do not require manual placement or routing. The design of such TRNGs is also highly portable within the same device family. This is not the case in many other TRNG designs. However, the design of a PLL-TRNGs is not a trivial task. Due to many PLL parameters, which need to be fine tuned to achieve required security and speed requirements, an exhaustive design space exploration is practically not feasible. Thus, the designers are required to go through many trial and error cycles of manual parameter tweaking and the results are still not guaranteed to be optimal. In this paper, we use a genetic algorithm (GA) based optimization to generate a suitable configuration of the PLL-TRNG, such that it is secure and reaches high output bit rate. GA optimization allows to take into account physical limits of the PLL, such as input/output frequency, and maximum voltage controlled oscillator (VCO) frequency, which avoids invalid configurations and reduces the development time. The method has proven to be very efficient and it significantly reduces the design time without compromising the security. All the presented configurations were tested on recent FPGA families and the statistical quality of the resulting TRNG configurations was verified using the AIS 31 test suite.
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Submitted on : Thursday, October 5, 2017 - 8:32:04 AM
Last modification on : Sunday, June 26, 2022 - 12:08:00 PM
Long-term archiving on: : Monday, January 8, 2018 - 6:41:16 AM


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Oto Petura, Ugo Mureddu, Nathalie Bochard, Viktor Fischer. Optimization of the PLL Based TRNG Design Using the Genetic Algorithm.. IEEE International Symposium on Circuits and Systems - ISCAS 2017, May 2017, Baltimore, MD, United States. pp.2202, ⟨10.1109/ISCAS.2017.8050839⟩. ⟨ujm-01575708⟩



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