LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment

ArXi:2511.12340v2 Announce Type: replace Efficient machine learning deployment requires models that account for hardware constraints. Because binary logic gates are the fundamental primitives of digital hardware, models built directly from logic operations offer a promising path toward highly energy-efficient computation. Recent work has shown that networks of binary logic gates can be trained with gradient-based optimization and that their wiring can be learned. However, existing approaches remain limited in scalability and