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tiny-dnn-on-starknet

Goal

Showing it is possible to run neural network inference on StarkNet.

How to run the demo?

Change directory to contracts/deployment and run python deploy_and_run.py

Overall approach

  1. Train the model, and split the model into Cairo contracts
  2. Deploy the contracts to StarkNet testnet, and perform address linkage - storing addresses of children contracts in their parent contracts
  3. Call the top-level contract to perform inference.

Contract architecture

  • Model: x => FC1 (W1:784x32) => v1 => RELU => h1 => FC2 (W2:32x10) => z
  • Divide x*W1 into 32 sum-of-products (sop), and divide each sop into 8 sub-sop, where first 7 sub-sops have 100 terms each and the 8th has 84 terms; don't forget the biases; each sub-sop is converted to a contract where the parameters are constants. Aggregator contracts are created for sop's and v1.
  • Create one contract for relu operation.
  • Divide h1*W2 into 10 sop's, each converted to a contract where the parameters are constants. Aggregator contract is created for z.
  • Create one top-level contract with exposed inference() function for external call.

How does the demo script interact with the contracts?

  • If calling top-level inference() directly, we get http time-out. Additionally, Cairo CPU is single-threaded, meaning we do not get inter-layer parallelism.
  • Instead, the demo script calls each sop in a parallel job. This trades off compute-latency with network-latency.
  • The part in the demo script that handles contract deployment and address linkage is commented out.

Next step

  1. conv layer
  2. recurrent networks
  3. backprop -- would enable on-chain learning and open a new world; have to deal with memory problem with activations