Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks

Researchers introduce Polynomial Surrogate Training (PST) to enable differentiable ternary logic gate networks, reducing parameters by 2,187x while maintaining performance. The method extends beyond binary logic gates to ternary systems with an UNKNOWN state for uncertainty handling, training 2-3x faster than binary networks.