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Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

Research Medical/Healthcare AI

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Overall 86

Content 95

Popularity 66

Observed public metrics from 1 member.

Merged summary

TL;DR - Differentiable Logic Gate Networks compile EEG classifiers into Boolean circuits, enabling competitive accuracy with substantially lower latency and memory use on edge hardware. This could make portable, real-time brain-computer interfaces more practical.

Achieved 80.2% Macro F1 in binary dementia detection, 6.8% above a matched-capacity MLP.
For 3-class emotion recognition, MLPs performed moderately better but had 2.3× higher latency and 14× larger models.
Diff-Logic inference stayed nearly constant across a 10× model-scale increase.
Peak CPU inference speedup reached 2.9× over MLPs on a 7W Jetson Orin Nano.

Sources (1)

Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices

arXiv cs.LG Shyamal Y. Dharia, Stephen D. Smith, Camilo E. Valderrama 2026-07-20 arXiv:2607.18149

Public signals Hugging Face upvotes 4

Providers: Hugging Face · Upvotes 4 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-20 14:37:03.447533 UTC

Achieved 80.2% Macro F1 in binary dementia detection, 6.8% above a matched-capacity MLP.
For 3-class emotion recognition, MLPs performed moderately better but had 2.3× higher latency and 14× larger models.
Diff-Logic inference stayed nearly constant across a 10× model-scale increase.
Peak CPU inference speedup reached 2.9× over MLPs on a 7W Jetson Orin Nano.

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