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Scaling up Trustless Neural Network Inference with Zero-Knowledge Proofs

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Scaling up Trustless Neural Network Inference with Zero-Knowledge Proofs

Duration: 00:13:19

Speaker: Yi Sun

Type: Talk

Expertise: Beginner

Event: Devcon 6

Date: Oct 2022

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

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About the speakers

YS

Yi Sun

ZKP enthusiast and open-source developer of ZK circuits for various crypto primitives (ECDSA, elliptic curve pairings).

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