ASIC implementation of random number generators using SR latches and its evaluation

By Naoya Torii, Hirotaka Kokubo, Dai Yamamoto, Kouichi Itoh, M. Takenaka, Tsutomu Matsumoto. 2016

20 citations recorded by the source corpus.

A true random number generator (TRNG) is proposed and evaluated by field-programmable gate arrays (FPGA) implementation that generates random numbers by exclusive-ORing (XORing) the outputs of many SR latches (Hata and Ichikawa, IEICE Trans. Inf. Syst. E95-D(2):426–436, 2012). This enables compact implementation and generates high-entropy random numbers.In this paper, we fabricate and evaluate 39 TRNGs using SR latches on 0.18 μm ASICs. Random numbers are generated by XORing the outputs of 256 SR latches. Our TRNGs pass the SP800-90B health tests and the AIS20/31 statistical tests in changing temperatures (from −20 to 60 °C) and voltages (1.80 ± 0.15 V). We also perform an independent and identically distributed (IID) test and calculate min-entropy according to the SP800-90B. With these tests, we are able to confirm that our TRNGs are highly robust against environmental stress. The power consumption and circuit scale of our TRNGs are 0.27 mW and 1240.5 gates, respectively. Our TRNGs that use SR latches are small enough to be implemented in embedded devices.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, ML Systems & Hardware, Signal Processing

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Citations in the corpus

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations (2016, background): “…function is stochastic: where σ is the “hard sigmoid” function: This stochastic binarization is more appealing theoretically (see Section 4) than the sign function, but somewhat harder to implement as it requires the hardware to generate random bits when quantizing (Torii et al., 2016).”
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation (2021, methodology): “Further in the forward pass, instead of using stochastic quantization which requires random seeds generating process [42], deterministic quantization are adopted and derived by setting the probability threshold to be p = 0.”