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https://github.com/zk-ml/tachikoma

neural network inference standard for zero-knowledge-proof systems
https://github.com/zk-ml/tachikoma

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neural network inference standard for zero-knowledge-proof systems

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 tachikoma: neural network inference standard for zero-knowledge-proof systems



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tachikoma defines how a neural network's inference process should be serialized into a graph of operator computational traces, each of which containing the input, expected output, relevant metadata (including parameters), and an identifier relating back to the original operator in TVM's intermediate representation.



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We are actively working on consolidating the standards into a stable form and release relevant artifacts, as well as forming a committee and organizing regular meetings. If you are interested in this effort, please reach out!



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in addition, tachikoma's TVM fork is useful for:

- converting a floating-point neural network or a framework-prequantized model into an integer-only form

- generating a computational trace binary respecting the tachikoma standard.

- as a proof of concept, how tachikoma can be used in ZKP systems. We will be implementing a simple graph runtime on top of the tachikoma standard, as well as a circuit builder in ZEXE. The code will be available here: https://github.com/zk-ml/tachikoma-poc-runtime