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首页> 外文期刊>International journal of circuit theory and applications >A novel high speed Artificial Neural Network-based chaotic True Random Number Generator on Field Programmable Gate Array
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A novel high speed Artificial Neural Network-based chaotic True Random Number Generator on Field Programmable Gate Array

机译：基于现场可编程门阵列的新型基于高速人工神经网络的混沌真随机数发生器

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摘要

It is well observed that cryptographic applications have great challenges in guaranteeing high security as well as high throughput. Artificial neural network (ANN)-based chaotic true random number generator (TRNG) structure has not been unprecedented in current literature. This paper provides a novel type of high-speed TRNG based on chaos and ANN implemented in a Xilinx field-programmable gate array (FPGA) chip. The paper consists of two main parts. In the first part, chaos analyses of Pehlivan-Uyaroglu_2010 chaotic system (PUCS) have been accomplished to prove that PUCS operates in chaotic regime. So PUCS can be an efficient alternative to the entropy source for classical TRNGs. In the second part, the hardware design of the proposed TRNG has been created using VHDL in Xilinx platform. As a result, the implemented TRNG offers throughput up to 115.794 Mbps. Besides, the generated random numbers have been tested with the FIPS 140-1 and NIST 800.22 test suites. The high quality of generated true random numbers have been confirmed by passing all randomness tests. The results have shown that the proposed system can provide not only high throughput but also high quality random bit sequences for a wide variety of embedded cryptographic applications.
机译：众所周知，密码应用程序在保证高安全性和高吞吐量方面面临巨大挑战。基于人工神经网络（ANN）的混沌真实随机数生成器（TRNG）结构在当前文献中并非空前。本文提供了一种基于Xilinx现场可编程门阵列（FPGA）芯片的基于混沌和ANN的新型高速TRNG。本文包括两个主要部分。在第一部分，完成了对Pehlivan-Uyaroglu_2010混沌系统（PUCS）的混沌分析，以证明PUCS在混沌状态下运行。因此，PUCS可以作为传统TRNG的熵源的有效替代方法。在第二部分中，已在Xilinx平台中使用VHDL创建了拟议的TRNG的硬件设计。结果，实现的TRNG提供高达115.794 Mbps的吞吐量。此外，生成的随机数已通过FIPS 140-1和NIST 800.22测试套件进行了测试。通过所有随机性测试已确认了生成的真实随机数的高质量。结果表明，所提出的系统不仅可以为多种嵌入式密码应用提供高吞吐量，而且还可以提供高质量的随机比特序列。

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