<ul><li>Researchers at Stanford University have introduced convolutional differentiable logic gate networks (LGNs) with logic gate tree kernels.</li><li>The research combines concepts from machine vision with differentiable logic gate networks, allowing for the training of deeper LGNs and improving training efficiency.</li><li>The proposed architecture, 'LogicTreeNet', decreases model size and improves accuracy compared to the state of the art.</li><li>The model achieves inference speeds of only 4 nanoseconds and improves accuracy on MNIST and CIFAR-10 datasets.</li></ul>

This New Logic Gate Network Reduces Inference Speed to Only 4 Nanoseconds

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