Entropy extraction in metastability-based TRNG

Vikram B. Suresh, W. Burleson
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引用次数: 36

Abstract

True Random Number Generators (TRNG) implemented in deep sub micron (DSM) technologies become biased in bit generation due to process variations and fluctuations in operating conditions. A variety of mechanisms ranging from analog and digital circuit techniques to algorithmic post-processing can be employed to remove bias. In this work we compare the effectiveness of digital post-processing using the XOR function and Von Neumann Corrector with circuit calibration technique for a meta-stability based reference TRNG design. The energy consumption per bit is used as the metric for comparison of the different techniques. The results indicate that the calibration technique is effective for 12% larger process variation than the XOR function and extracts entropy comparable to the Von Neumann Corrector at 56% lesser energy/bit. The analysis thereby demonstrates that circuit calibration provides an efficient tradeoff between entropy and energy/bit for removing bias in lightweight TRNG.
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基于亚稳的TRNG中的熵提取
由于工艺变化和操作条件的波动,在深亚微米(DSM)技术中实现的真随机数发生器(TRNG)在比特生成方面存在偏差。从模拟和数字电路技术到算法后处理的各种机制都可以用来消除偏置。在这项工作中,我们比较了使用异或函数和冯·诺伊曼校正器与电路校准技术的数字后处理的有效性,用于基于亚稳定的参考TRNG设计。每比特的能量消耗被用作比较不同技术的度量。结果表明,与异或函数相比,该校准技术在过程变化大12%的情况下是有效的,并且以比冯·诺伊曼校正器低56%的能量/比特提取与之相当的熵。因此,分析表明,电路校准为消除轻量级TRNG中的偏置提供了熵和能量/比特之间的有效权衡。
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