| title | Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices |
|---|---|
| authors | Shyamal Y. Dharia, Stephen D. Smith, Camilo E. Valderrama |
| date | 2026-07-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.18149 |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Real-time EEG classification on edge devices is bottlenecked by the floating-point arithmetic of conventional neural networks. We investigated Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative that compiles models into pure Boolean circuits executable via bitwise CPU operations. Through rigorous iso-parameter experiments across four EEG datasets spanning two classification tasks, binary dementia detection and 3-class emotion recognition, we compared Diff-Logic against matched-capacity Multi-Layer Perceptron (MLP) and Binarized Neural Network (BNN) baselines at four complexity tiers (50k-500k parameters). On dementia screening, Diff-Logic achieved 80.2% Macro F1, outperforming the MLP baseline by 6.8%. On emotion recognition, the MLP retained a moderate performance advantage but incurred a 2.3times higher latency and 14times larger model size when deployed on a power-constrained (7W) Nvidia Jetson Orin Nano CPU (Single-core). Critically, Diff-Logic inference time remained nearly constant across a 10times increase in model scale, achieving a peak speedup of 2.9times over MLPs at the largest complexity tier. Our results establish logic-based neural architectures as a practical paradigm for resource-constrained brain-computer interfaces, achieving competitive or superior performance while natively satisfying the latency and memory constraints of portable edge deployment. Code is available on GitHub: https://github.com/Shyamal-Dharia/eeg-difflogic |
cite
@misc{dharia2026differentiable,
title={Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices},
author={Shyamal Y. Dharia and Stephen D. Smith and Camilo E. Valderrama},
year={2026},
eprint={2607.18149},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.18149}
}