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:


I. research / repository for machine learning algorithms and systems on emergent runtimes

II. tachikoma / neural network inference standard for zero-knowledge-proof systems and its reference compiler implementation

III. uchikoma / essentialist machine learning transpiler for non-floating-point runtimes

Contributors

Modulus Labs
Cortex Labs