ButterflyMoE: Sub-Linear Ternary Experts via Structured Butterfly Orbits

ButterflyMoE introduces a breakthrough approach to reduce memory requirements for AI expert models by 150× through geometric parameterization instead of storing independent weight matrices. The method uses shared ternary prototypes with learned rotations to achieve sub-linear memory scaling, enabling deployment of multiple experts on edge devices.