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We present a methodology to design a new class of low-complexity entropy estimators, aimed at designing tunable True Random Number Generators (TRNGs). Our design approach is detailed for the typical scenario of non-IID ergodic sources, analyzing how source memory impacts estimation precision. Additionally, we have examined the refined hardware implementation of this estimator, specifically for a Xilinx Artix 7 FPGA.

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Digital Low-Complexity Entropy Estimator Based on the Direct Assessment of Average Shannon Entropy

  • 会议论文

  • 发表日期：

    2025年

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  • Tommaso Addabbo,
  • Ada Fort,
  • Filippo Spinelli,
  • Valerio Vignoli

摘要

We present a methodology to design a new class of low-complexity entropy estimators, aimed at designing tunable True Random Number Generators (TRNGs). Our design approach is detailed for the typical scenario of non-IID ergodic sources, analyzing how source memory impacts estimation precision. Additionally, we have examined the refined hardware implementation of this estimator, specifically for a Xilinx Artix 7 FPGA.