binarized-neural-networks

This paper introduces a PyTorch-based framework for pruning binarized neural networks and proposes a novel global weighting pruning method that achieves superior accuracy and pruning rates, enabling efficient deployment on edge hardware like FPGAs.

This paper introduces Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative to conventional neural networks for real-time EEG classification on edge devices, achieving competitive performance with significantly lower latency and model size.