Scaling up Trustless Neural Network Inference with Zero Knowledge Proofs

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https://archive.devcon.org/archive/watch/6/scaling-up-trustless-neural-network-inference-with-zero-knowledge-proofs/index

We present the first ZK-SNARK proof of valid inference for a full resolution ImageNet model. We will describe the arithmetization and quantization optimizations enabling us to SNARK large neural networks as well as a software package enabling transpilation from off-the-shelf models to halo2 circuits. We design protocols using our circuits to verify machine learning model predictions and accuracy and present concrete estimates of overhead costs based on our circuit implementations. This is joint

Speaker(s): Yi Sun
Skill level: Beginner
Track: ZKPs: Privacy, Identity, Infrastructure, & More
Keywords: zk

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