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This work investigates harnessing chaotic balanced states in spiking neural networks for low-power neuromorphic pseudo-random number generation (NPRNGs). A leaky-integrate-and-fire neuron model was implemented on FPGAs, achieving 3.24 mW power consumption and 120 kbps throughput. Both hardware and software instantiations produced high-quality random numbers, validated by standard statistical tests.
Brain-inspired chaotic neural networks yield high-quality random numbers at milliwatt power budgets.
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.