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LuminAIR is being developed in carefully planned phases to address the challenges of proving large-scale machine learning models while enabling practical use cases early on. Below is the roadmap outlining our phased approach to building and expanding LuminAIR.

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🏗️ Phase 1: Supporting Primitive Operators

In this foundational phase, LuminAIR focuses on supporting a minimal set of 11 primitive operators, which are sufficient to implement a wide range of machine learning models, such as linear regression, convolutional networks, and transformers.
OperatorStatus
Log2✅
Exp2✅
Sin✅
Sqrt✅
Recip✅
Add✅
Mul✅
Mod✅
LessThan✅
SumReduce✅
MaxReduce✅
Contiguous✅
  • These operators are implemented via the PrimitiveCompiler, a subset of the StwoCompiler.
  • Each operator requires a corresponding specialized component in the AIR (Algebraic Intermediate Representation).
This phase currently under active development 🏗️.

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🔒 Phase 2: Optimizations and Accessibility

This phase focuses on improving performance and developer experience by introducing fused compilers, specialized operators, and easier integration tools.
A small primitive operator set can lead to computational graphs with hundreds or thousands of operations, making them slow to execute and prove.To address this, we will:
  • Develop compilers that fuse multiple primitive operations into efficient composite operators.
  • Add support for common ML operators like MatMul, SoftMax, and ReLU.
  • Extend the AIR with specialized components for these new operators.
Currently, LuminAIR is written in Rust, requiring users to interact with it through Rust code. A Python SDK will be developed to make LuminAIR more accessible to data scientists and ML practitioners who are more familiar with Python.
This task will enable proof verification directly in web browsers using WebAssembly (WASM).

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🔒 Phase 3: Decentralized Verification and GPU Support

This phase aims to bring LuminAIR proofs into decentralized ecosystems and enhance performance through GPU acceleration.
Deploy the LuminAIR verifier as a smart contract for decentralized proof verification. Possible approaches include:
  • Implementing the verifier as a Cairo program verified on Starknet.
  • Leveraging Ethereum’s crypto-economic security via AVSs networks like Aligned Layer.
Integrate support for Icicle-Stwo, which enables GPU acceleration via CUDA backends. This will significantly improve the speed of proof generation for large-scale models

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🔒 Phase 4: Future Enhancements

Details for this phase are yet to be finalized but may include:
  • Support for ONNX graph.
  • Continuation mechanism, allowing the proof to be divided into several parts that can be proved in parallel.
  • Expanding compatibility with additional ZK backends.
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