A Design Procedure for Oscillator-Based Hardware Random Number Generator with Stochastic Behavior Modeling

2011 ◽  
pp. 107-121 ◽  
Author(s):  
Takehiko Amaki ◽  
Masanori Hashimoto ◽  
Yukio Mitsuyama ◽  
Takao Onoye
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Design Procedure ◽  
Behavior Modeling ◽  
Number Generator ◽  
Stochastic Behavior

scholarly journals An Asynchronous and Low-Power True Random Number Generator Using STT-MTJ

2020 ◽  
Author(s):  
Ben Perach ◽  
Shahar Kvatinsky
Keyword(s):  
Low Power ◽  
Random Number ◽  
Random Number Generator ◽  
Low Frequency ◽  
Magnetic Tunnel Junction ◽  
Equation System ◽  
Spin Transfer Torque ◽  
Number Generator ◽  
Stochastic Behavior ◽  
Basic Module

<div>The Spin Transfer Torque Magnetic Tunnel Junction</div><div>(STT-MTJ) is an emerging memory technology whose interesting</div><div>stochastic behavior might benefit security applications. In this</div><div>paper, we leverage this stochastic behavior to construct a true</div><div>random number generator (TRNG), the basic module in the</div><div>process of encryption key generation. Our proposed TRNG</div><div>operates asynchronously and thus can use small and fast STT</div><div>MTJ devices. As such, it can be embedded in low-power and</div><div>low-frequency devices without loss of entropy. We evaluate</div><div>the proposed TRNG using a numerical simulation, solving the</div><div>Landau–Lifshitz–Gilbert (LLG) equation system of the STTMTJ</div><div>devices. Design considerations, attack analysis, and process</div><div>variation are discussed and evaluated. The evaluation shows that</div><div>our solution is robust to process variation, achieving a Shannonentropy</div><div>generating rate between 99.7Mbps and 127.8Mbps for</div><div>90% of the instances.</div>

scholarly journals An Asynchronous and Low-Power True Random Number Generator Using STT-MTJ

2020 ◽  
Author(s):  
Ben Perach ◽  
Shahar Kvatinsky
Keyword(s):  
Low Power ◽  
Random Number ◽  
Random Number Generator ◽  
Low Frequency ◽  
Magnetic Tunnel Junction ◽  
Equation System ◽  
Spin Transfer Torque ◽  
Number Generator ◽  
Stochastic Behavior ◽  
Basic Module

<div>The Spin Transfer Torque Magnetic Tunnel Junction</div><div>(STT-MTJ) is an emerging memory technology whose interesting</div><div>stochastic behavior might benefit security applications. In this</div><div>paper, we leverage this stochastic behavior to construct a true</div><div>random number generator (TRNG), the basic module in the</div><div>process of encryption key generation. Our proposed TRNG</div><div>operates asynchronously and thus can use small and fast STT</div><div>MTJ devices. As such, it can be embedded in low-power and</div><div>low-frequency devices without loss of entropy. We evaluate</div><div>the proposed TRNG using a numerical simulation, solving the</div><div>Landau–Lifshitz–Gilbert (LLG) equation system of the STTMTJ</div><div>devices. Design considerations, attack analysis, and process</div><div>variation are discussed and evaluated. The evaluation shows that</div><div>our solution is robust to process variation, achieving a Shannonentropy</div><div>generating rate between 99.7Mbps and 127.8Mbps for</div><div>90% of the instances.</div>

Fuzzy Based Pseudo Random Number Generator used for Wireless Networks

2013 ◽  
Vol 16 (2) ◽  
pp. 210-216 ◽  
Author(s):  
Sattar B. Sadkhan ◽  
◽  
Sawsan K. Thamer ◽  
Najwan A. Hassan ◽  
◽  
...  
Keyword(s):  
Wireless Networks ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

0.4 mW, 0.27 pJ/bit true random number generator using jitter, metastability and current starved topology

2020 ◽  
Vol 14 (7) ◽  
pp. 1001-1011
Author(s):  
Dhirendra Kumar ◽  
Rahul Anand ◽  
Sajai Vir Singh ◽  
Prasanna Kumar Misra ◽  
Ashok Srivastava ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
True Random Number Generator

scholarly journals A High‐Speed True Random Number Generator Based on a Cu x Te 1− x Diffusive Memristor

2021 ◽  
pp. 2100062
Author(s):  
Kyung Seok Woo ◽  
Jaehyun Kim ◽  
Janguk Han ◽  
Jin Myung Choi ◽  
Woohyun Kim ◽  
...  
Keyword(s):  
High Speed ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
True Random Number Generator

scholarly journals A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Micromachines ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Junxiu Liu ◽  
Zhewei Liang ◽  
Yuling Luo ◽  
Lvchen Cao ◽  
Shunsheng Zhang ◽  
...  
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Logistic Map ◽  
Random Number Generator ◽  
Alternative Methods ◽  
Random Numbers ◽  
Number Generator ◽  
Stochastic Computing ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

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