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

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).

Automated summary

In this paper, a memristive Hopfield neural network (MHNN) is proposed, which is implemented by adding a suitable memristor to the Hopfield neural network (HNN). A new pseudo-random number generator (PRNG) based on MHNN is also developed. The experiments show the randomness and high performance of the PRNG. Additionally, an image encryption system based on PRNG is proposed and implemented on FPGA.