Sometimes division operators cannot be avoided in AI models, which are more complex than addition, subtraction, or multiplication. Use an extra matrix R to store the division remainder, and utilize this remainder to avoid the division.
In this section, we briefly describe the project architecture, the GitHub directory structure is as follows:
.
├── circuits/
│ ├── Arithmetic.circom
│ ├── circomlib/
│ ├── circomlib-matrix/
│ ├── operators
│ └── util.circom
├── contracts/
├── frontend/
├── integer_only_gan/
├── main.py
├── python/
└── README.md
Model training and quantization to TVM are located in interger_only_gan.
Model translator and inference engine are located in `python` and the cmd is the `main.py`, print usage with command -h.
Circom ML operators are located circuits/operators.
The frontend demo is located infrontend.
contract debug and deploy code is located in contracts.
Our goal for this competition is AIGC, but the application future is much more than that. Like the infinite rooms of AI models, our project has a profound influence on expanding the capabilities of blockchains:
(Shown) Vision models -> AIGCLanguage models -> chatbot, writing assistantLinear models and decision trees -> fraud detection, Sybil attack preventionMulti-modal models -> recommender systems
And some scenarios are considered for reference:
Converting machine learning models to zero-knowledge proofs, enabling people in underdeveloped countries to generate income with on-chain AIGC or ZK-ML-enhanced trustless freelancing.Governance tech in consensus is tricky. Our tools can enhance existing voting and Sybil resistance techniques by allowing an ML-based approach: imagine a self-evolving DAO smart contract powered by a neural network.Wash-trading detectors in DEX.Provable biometric IDs. Like in Worldcoin.AI Oracles which can verify off-chain world data. Like steps, health data, environmental data, etc.AI competitions. People can commit their model weights’ hash first and the inputs can be revealed later.GameFi NPCs. We can have AI characters instead of plain old scripted NPCs.Dynamic tokenomics. With AI-controlled tokenomics, we can have potentially better algo-stablecoins.AI DAOs. AI can participate in DAO’s decision makings.Automated traders. Although on-chain AI will most likely fail in competition with real-world Hedge funds, it can be a showcase.Anti-fraud in DeFi. Like in lending protocols and insurance protocols.NFTs. Like what we’ve built in this project.Self-evolving Blockchains. Ultimately, ZKML can be used to determine crucial parameters of , such as block interval, block size, and block rewards, based on collected data.
More possibilities are in your imagination.
vCNNZEN with GitHubzkCNN with GitHubMystiquezk-ml(2021)CircomLib-ML(2022)zk-mnist(2022) with Blogproto-neural-zkp(2022)2PAI(2022)