Diff-Logic: Low-Latency EEG Classification on Edge Devices via Logic Gate Networks
Shyamal Y. Dharia · hf · 2026-07-24
To address the bottleneck of floating-point arithmetic in conventional neural networks for real-time EEG classification on edge devices, researchers investigated Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative. This approach compiles models into pure Boolean circuits executable via bitwise CPU operations.
Key Findings:
- Dementia Detection: Diff-Logic achieved an 80.2% Macro F1 score, outperforming parameter-matched MLPs by 6.8%.
- Emotion Recognition: While MLPs retained a moderate performance advantage, they incurred 2.3x higher latency and were 14x larger when deployed on a 7W Nvidia Jetson Orin Nano.
- Scalability: Diff-Logic inference time remained nearly constant across a 10x increase in model scale, achieving a 2.9x speedup over MLPs at the largest complexity tier.
The study establishes logic-based neural architectures as a practical paradigm for resource-constrained brain-computer interfaces, natively satisfying the strict latency and memory limits of portable edge deployment. Code is open-sourced on GitHub.
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