Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
This enables practical client-side zero-knowledge ML applications with EVM smart contract compatibility, though it involves increased preprocessing costs.
The paper tackles the problem of slow proving times for zero-knowledge machine learning by introducing Bionetta, which achieves significant speed improvements enabling neural network proofs on mobile devices.
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.