中国物理B ›› 2022, Vol. 31 ›› Issue (2): 20505-020505.doi: 10.1088/1674-1056/ac3cb2

所属专题： SPECIAL TOPIC — Interdisciplinary physics: Complex network dynamics and emerging technologies

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FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

Fei Yu(余飞)1,†, Zinan Zhang(张梓楠)1, Hui Shen(沈辉)1, Yuanyuan Huang(黄园媛)1, Shuo Cai(蔡烁)1, and Sichun Du(杜四春)2   

  1. 1 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China;
    2 College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
  • 收稿日期:2021-08-27 修回日期:2021-11-08 接受日期:2021-11-24 出版日期:2022-01-13 发布日期:2022-01-18
  • 通讯作者: Fei Yu E-mail:yufeiyfyf@csust.edu.cn
  • 基金资助:
    Project supported by the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 21B0345), the Postgraduate Scientific Research Innovation Project of Changsha University of Science and Technology (Grant Nos. CX2021SS69 and CX2021SS72), the Postgraduate Scientific Research Innovation Project of Hunan Province, China (Grant No. CX20200884), the Natural Science Foundation of Hunan Province, China (Grant Nos. 2019JJ50648, 2020JJ4622, and 2020JJ4221), the National Natural Science Foundation of China (Grant No. 62172058), and the Special Funds for the Construction of Innovative Provinces of Hunan Province, China (Grant Nos. 2020JK4046 and 2022SK2007).

FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

Fei Yu(余飞)1,†, Zinan Zhang(张梓楠)1, Hui Shen(沈辉)1, Yuanyuan Huang(黄园媛)1, Shuo Cai(蔡烁)1, and Sichun Du(杜四春)2   

  1. 1 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China;
    2 College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
  • Received:2021-08-27 Revised:2021-11-08 Accepted:2021-11-24 Online:2022-01-13 Published:2022-01-18
  • Contact: Fei Yu E-mail:yufeiyfyf@csust.edu.cn
  • Supported by:
    Project supported by the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 21B0345), the Postgraduate Scientific Research Innovation Project of Changsha University of Science and Technology (Grant Nos. CX2021SS69 and CX2021SS72), the Postgraduate Scientific Research Innovation Project of Hunan Province, China (Grant No. CX20200884), the Natural Science Foundation of Hunan Province, China (Grant Nos. 2019JJ50648, 2020JJ4622, and 2020JJ4221), the National Natural Science Foundation of China (Grant No. 62172058), and the Special Funds for the Construction of Innovative Provinces of Hunan Province, China (Grant Nos. 2020JK4046 and 2022SK2007).

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摘要： A memristive Hopfield neural network (MHNN) with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network (HNN) with a special activation gradient. The MHNN is simulated and dynamically analyzed, and implemented on FPGA. Then, a new pseudo-random number generator (PRNG) based on MHNN is proposed. The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator, which effectively ensures the randomness of PRNG. The experiments in this paper comply with the IEEE 754-1985 high precision 32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7Z020CLG400-2 FPGA chip and the Verilog-HDL hardware programming language. The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis, proving its randomness and high performance. Finally, an image encryption system based on PRNG is proposed and implemented on FPGA, which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things (IoT).

关键词: memristive Hopfield neural network (MHNN), pseudo-random number generator (PRNG), FPGA, image encryption, decryption system

Abstract: A memristive Hopfield neural network (MHNN) with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network (HNN) with a special activation gradient. The MHNN is simulated and dynamically analyzed, and implemented on FPGA. Then, a new pseudo-random number generator (PRNG) based on MHNN is proposed. The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator, which effectively ensures the randomness of PRNG. The experiments in this paper comply with the IEEE 754-1985 high precision 32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7Z020CLG400-2 FPGA chip and the Verilog-HDL hardware programming language. The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis, proving its randomness and high performance. Finally, an image encryption system based on PRNG is proposed and implemented on FPGA, which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things (IoT).

Key words: memristive Hopfield neural network (MHNN), pseudo-random number generator (PRNG), FPGA, image encryption, decryption system

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
05.45.Pq (Numerical simulations of chaotic systems) 05.45.Vx (Communication using chaos) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)