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

TL;DR

LILogicNet introduces a scalable, differentiable logic gate network with structured sparsity, enabling efficient training and deployment of binary logic models on hardware with minimal overhead.

Contribution

The paper presents a novel differentiable connectivity mechanism and architecture that significantly improves the scalability and efficiency of logic gate networks for machine learning.

Findings

Achieves 98.45% accuracy on MNIST with only 8,000 gates in under five minutes.

Surpasses prior logic-gate models on CIFAR-10 with 256,000 gates, reaching 60.98% accuracy.

Enables fully binarized, hardware-efficient inference across digital platforms.

Abstract

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 training efficiency. We address these challenges by treating the network connectome as a differentiable object and introducing a Top-K connectivity mechanism that enforces structured sparsity during training. Our resulting architecture, LILogicNet, substantially improves the efficiency of logic-gate networks. A model with only 8,000 gates trains on MNIST in under five minutes while achieving 98.45% test accuracy, matching the performance of state-of-the-art logic-gate models that require two orders of magnitude more gates. At larger scales, a 256,000-gate model achieves 60.98% test accuracy on CIFAR-10, surpassing prior approaches with comparable gate budgets. Because the final model is fully binarized and composed entirely of logic operations, inference incurs minimal compute overhead and maps naturally to a wide range of digital hardware platforms, enabling efficient deployment across diverse computing systems.