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:
repository for machine learning algorithms and systems on emergent runtimes
neural network inference standard for zero-knowledge-proof systems and its reference compiler implementation
essentialist machine learning transpiler for non-floating-point runtimes