# AI-Hybrid TRNG: Kernel-Based Deep Learning for Near-Uniform Entropy Harvesting from Physical Noise

arXiv: https://arxiv.org/abs/2507.00145 · HTML: https://mlsanity.com/paper/2507.00145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing](https://mlsanity.com/paper/2207.14131/) (2022)
- [A Search for Good Pseudo-random Number Generators : Survey and Empirical Studies](https://mlsanity.com/paper/1811.04035/) (2018)
- [Ensuring High-Quality Randomness in Cryptographic Key Generation](https://mlsanity.com/paper/1309.7366/) (2013)
- [Random Shifting Intelligent Reflecting Surface for OTP Encrypted Data Transmission](https://mlsanity.com/paper/2010.14268/) (2020)
- [RHS-TRNG: A Resilient High-Speed True Random Number Generator Based on STT-MTJ Device](https://mlsanity.com/paper/2312.17453/) (2023)

## Abstract (arXiv)

AI-Hybrid TRNG is a deep-learning framework that extracts near-uniform entropy directly from physical noise, eliminating the need for bulky quantum devices or expensive laboratory-grade RF receivers. Instead, it relies on a low-cost, thumb-sized RF front end, plus CPU-timing jitter, for training, and then emits 32-bit high-entropy streams without any quantization step. Unlike deterministic or trained artificial intelligence random number generators (RNGs), our dynamic inner-outer network couples adaptive natural sources and reseeding, yielding truly unpredictable and autonomous sequences. Generated numbers pass the NIST SP 800-22 battery better than a CPU-based method. It also passes nineteen bespoke statistical tests for both bit- and integer-level analysis. All results satisfy cryptographic standards, while forward and backward prediction experiments reveal no exploitable biases. The model's footprint is below 0.5 MB, making it deployable on MCUs and FPGA soft cores, as well as suitable for other resource-constrained platforms. By detaching randomness quality from dedicated hardware, AI-Hybrid TRNG broadens the reach of high-integrity random number generators across secure systems, cryptographic protocols, embedded and edge devices, stochastic simulations, and server applications that need randomness.