Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
Abstract
Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers. The brain, on the other hand, efficiently generates unpredictable output complex network dynamics occurring in a high-dimensional state. This state, which is hypothesized to be chaotic, relies on the balance between excitation and inhibition. Here, we investigated if computational models of these chaotic balanced states can be harnessed for Neuromorphic Pseudo-Random Number Generators (NPRNGs) in low power hardware. We successfully constructed a balanced spiking neural network model consisting of leaky-integrate-and-fire neurons that could be readily implemented in low power FPGAs and used as a NPRNG. The prototyped NPRNG consumed 3.24 mW during operation and produced pseudo-random numbers at 120kbps. In both hardware and software instantiations, NPRNGs produce high-quality random numbers as validated by standard metrics for testing RNG quality.
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BibTeX
@misc{shamsi2026neuromorphic,
title = {{Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation}},
author = {Jafar Shamsi and Navid Akbari and Sonia Sennik and Aaron Gruber and Wilten Nicola},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00719},
eprint = {2610.00719},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00719},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
AU - Jafar Shamsi
AU - Navid Akbari
AU - Sonia Sennik
AU - Aaron Gruber
AU - Wilten Nicola
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.00719
UR - https://arxiv.org/abs/2610.00719
AB - Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers. The brain, on the other hand, efficiently generates unpredictable output complex network dynamics occurring in a high-dimensional state. This state, which is hypothesized to be chaotic, relies on the balance between excitation and inhibition. Here, we investigated if computational models of these chaotic balanced states can be harnessed for Neuromorphic Pseudo-Random Number Generators (NPRNGs) in low power hardware. We successfully constructed a balanced spiking neural network model consisting of leaky-integrate-and-fire neurons that could be readily implemented in low power FPGAs and used as a NPRNG. The prototyped NPRNG consumed 3.24 mW during operation and produced pseudo-random numbers at 120kbps. In both hardware and software instantiations, NPRNGs produce high-quality random numbers as validated by standard metrics for testing RNG quality.
ER -