TWIG: Tree-structured Weights for Efficient Hardware-Friendly Neural Networks

TWIG represents neural network weights as paths of discrete decisions, such as signs, power-of-two shifts, or shared table indices. This structure enables weight layers to function as circuits of wires and adders, optimizing them for fixed-weight hardware fabrication and unifying pruning, quantization, and factorization into single object edits.