In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads.
@article{arxiv.2510.06784,
title = {Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving},
author = {Dmytro Zakharov and Oleksandr Kurbatov and Artem Sdobnov and Lev Soukhanov and Yevhenii Sekhin and Vitalii Volovyk and Mykhailo Velykodnyi and Mark Cherepovskyi and Kyrylo Baibula and Lasha Antadze and Pavlo Kravchenko and Volodymyr Dubinin and Yaroslav Panasenko},
journal= {arXiv preprint arXiv:2510.06784},
year = {2025}
}