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Linear A makes machine learning viable on emergent execution environments.

We are witnessing a marvel of machine learning transforming human productivity and creativity. However, software and hardware restrictions make it nearly impossible for developers to harness these capabilities in emergent execution environments, notably zero-knowledge-proof systems.

Linear A addresses the problems faced by this burgeoning trio of machine learning practitioners, system architects, and hardware designers through open-access, collaboratively-engineered initiatives.

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  1. research research Public

    repository for machine learning algorithms and systems on emergent runtimes

    Python 21 1

  2. tachikoma tachikoma Public

    Forked from apache/tvm

    neural network inference standard for zero-knowledge-proof systems

    Python 34 2

  3. uchikoma uchikoma Public

    essentialist neural network transpiler for non-floating-point runtimes

    Python 29 2

  4. Halide Halide Public

    Forked from halide/Halide

    a language for fast, portable data-parallel computation

    C++

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