inference-labs-inc/zkml-blueprints resource

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1868 stars · 27 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated collection of mathematical formulations and zero-knowledge circuit designs for proving machine learning computations, covering operations like matrix multiplication, ReLU, range checks, and pooling. It provides PDF blueprints with rigorous constraint explanations intended as a reference for zkML and zk-SNARK practitioners.

Use cases

• design zero-knowledge circuits for machine learning inference
• learn how to encode ReLU and matrix multiplication as ZK constraints
• find reference formulations for quantized matmul in zk-SNARK circuits
• build privacy-preserving verifiable ML proofs
• study constraint system design for zkML
• implement range checks and pooling operations in ZK circuits

When to choose

• you need rigorous mathematical references for zkML circuit primitives
• you are designing zk-SNARK constraints for neural network layers
• you want documented best practices for provable ML computations

When to avoid

• you need runnable code or a working zkML framework rather than design documents
• you need circuits for conv layers or pooling that are not yet published
• you want a turnkey zero-knowledge ML inference tool

Facets

learning-resource · maturity active

cryptography machine-learning documentation math machine-learning privacy tutorials cross-platform zero-knowledge-proofs zkml zk-snarks circuit-design constraint-systems privacy-preserving-ml blueprints cryptography algorithms

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