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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map.

Junxiu Liu1, Zhewei Liang1, Yuling Luo1,2

  • 1School of Electronic Engineering, Guangxi Normal University, Guilin 541004, China.

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|January 5, 2021
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Summary
This summary is machine-generated.

This study introduces an efficient hardware pseudo-random number generator (PRNG) using an optimized chaotic map. The novel design significantly reduces hardware resource utilization by 89% while ensuring high security performance.

Keywords:
FPGAchaoslogistic mapstochastic computing

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Area of Science:

  • Computer Engineering
  • Cryptography
  • Chaos Theory

Background:

  • Chaotic maps offer alternative methods for pseudo-random number generation.
  • Hardware implementations of pseudo-random number generators (PRNGs) are crucial for secure applications.
  • Digital chaos degradation is a challenge in implementing chaotic maps.

Purpose of the Study:

  • To propose an efficient hardware pseudo-random number generator (PRNG) based on an optimized chaotic map.
  • To reduce hardware utilization while maintaining robust security performance.
  • To validate the generated pseudo-random numbers using standard statistical tests.

Main Methods:

  • Optimized a one-dimensional logistic map using perturbation operations.
  • Employed stochastic computing for hardware PRNG design.
  • Implemented the proposed PRNG on a Field Programmable Gate Array (FPGA) device.

Main Results:

  • The optimized chaotic map demonstrated good security performance.
  • Generated pseudo-random numbers passed the TestU01 and NIST SP 800-22 tests.
  • Achieved an 89% reduction in hardware resource utilization compared to conventional methods.

Conclusions:

  • The proposed hardware PRNG offers an efficient and secure solution.
  • Perturbation operations effectively mitigate digital chaos degradation.
  • The design presents a significant advancement in low-resource, high-performance PRNGs.