Related articles; re: rounds and layers:

- "Why are neural networks and cryptographic ciphers so similar? (2025)" https://reiner.org/neural-net-ciphers .. https://news.ycombinator.com/item?id=47983467

- "Show HN: ResonanceNet – Proof-of-Training Blockchain" https://news.ycombinator.com/item?id=47386901

- "Cryptographic hashing as a transformer attention head" https://github.com/ffr1/unbounded-context-attention

- 'Implementation for NDSS'2025 paper: "TensorCrypt: Repurposing Neural Networks for Efficient Cryptographic Computation"' https://github.com/OSUSecLab/TensorCrypt :

> More specifically, with a program translation framework that converts traditional cryptographic algorithms into NN models, our proof-of-concept implementations in TensorFlow demonstrate substantial performance improvements: encryption speeds for AES, Chacha20, and Salsa20 show increases of up to 4.09×, 5.44×, and 5.06×, respectively, compared to existing GPU-based cryptographic solutions written by human experts.