MNIST

MNIST example is using the Onnx Precompile and is available in two versions 0xe49b0C4107238674c5BC43167b9D3b93954c0294 and 0x3ED7196f54c95197C89cb43fF2fc1002eaaf0D6e.

0xe49b0C4107238674c5BC43167b9D3b93954c0294 is an example of deployment where your model is too big to fit inside a contract as a constant so has to be read from storage. It allows for bigger models but as a tradeoff it costs more gas to call the model given the cost of reading contract storage.

0x3ED7196f54c95197C89cb43fF2fc1002eaaf0D6e is an example of deployment where your model fits in a contract. The gas usage per inference in that case is around 8x cheaper than the storage version but limits the model size to around 48kBs

It allows for atomic on chain inference of 28x28 images of hand drawn digits. The inputs are represented as an array of 728 integers where each integer represents the grayscale value of the image's pixel. While the user can submit any type of image to the contract the output for images other than digits won't make much sense. The images can be of any dimensions as long as you can pay for the gas cost. Currently this only cost of this contract is reading the model from storage, inference itself doesn't cost any gas however this will change in the future when we implement more models and estimate their gas usage accordingly. The contract code looks as follows

interface NeuralOnnx {
    function InferFloat32(
        int[] memory data,
        bytes memory model,
        uint8 precision
    ) external returns (int[] memory);
}


contract Mnist {
    NeuralOnnx onnx = NeuralOnnx(0x0000000000000000000000000000000000000044);

    bytes private model;

    event InferenceResult(uint, int[]);

    // Model is too big to deploy as a constant/immutable but can be set as a storage variable
    //bytes private constant model =
    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    constructor(bytes memory _model) {
        model = _model;
    }

    function infer(int[] calldata input) public returns (uint, int[] memory) {
        int[] memory out = onnx.InferFloat32(input, model, 8);
        uint guess = 0;
        int best = out[0];
        //Find which digit has the highest score
        for (uint i = 1; i < 10; i++) {
            if (out[i] > best) {
                best = out[i];
                guess = i;
            }
        }
        emit InferenceResult(guess, out);
        return (guess, out);
    }
}