FPGA Implementation of a Multi-PRNG Based on a Multiscroll Chaotic Hopfield Neural Network

Pseudorandom number generators (PRNGs) are fundamental components in cryptographic algorithms. The new concept of multi-PRNG introduced in this article consists of a unique generator capable of producing multiple streams of pseudorandom numbers. Multiscroll chaotic systems are known for generating multiple scrolls within a single attractor. With the aforementioned this article introduces a novel field-programmable gate array (FPGA) implementation of a multi-PRNG based on a multiscroll chaotic memristive Hopfield neural network (MHNN). The main contribution of this work is the generation of multiple spatially dependent PRNG streams from a chaotic multiscroll system by dividing the phase space of the attractor into sub-phase spaces. Each scroll in the multiscroll attractor functions as an independent PRNG. This innovative approach to generating multiple PRNGs from multiscroll chaotic systems is unprecedented in the existing literature. The 5-D MHNN chaotic model used in this work employs hyperbolic tangent and sine functions, which were implemented through a hardware-efficient CORDIC approach. Besides, The FPGA implementation to produce the chaotic time series leverages the Euler method with 32-bit fixed-point arithmetic, selected for its simplicity and low resource utilization. Finally, The randomness of the binary sequences produced by the multi-PRNG is rigorously validated using the NIST SP 800-22a and TestU01 suites, confirming their potential for cryptographic applications.