Differentiable Logic Gate Networks for Low-Latency EEG Classification on Edge Devices
Diff-Logic matched or beat larger neural baselines on EEG tasks while cutting edge-device latency and model size.
The paper tests Differentiable Logic Gate Networks against matched MLP and BNN baselines across four EEG datasets. On dementia screening, Diff-Logic reached 80.2% Macro F1, 6.8% above the MLP baseline. On emotion recognition, MLPs still performed better, but used 2.3x more latency and 14x more model size on a 7W Jetson Orin Nano CPU. Diff-Logic inference stayed nearly flat as model scale rose 10x, with a peak 2.9x speedup over MLPs. ArXiv · AI/CL/LG's note
The paper tests Differentiable Logic Gate Networks against matched MLP and BNN baselines across four EEG datasets. On dementia screening, Diff-Logic reached 80.2% Macro F1, 6.8% above the MLP baseline. On emotion recognition, MLPs still performed better, but used 2.3x more latency and 14x more model size on a 7W Jetson Orin Nano CPU. Diff-Logic inference stayed nearly flat as model scale rose 10x, with a peak 2.9x speedup over MLPs. ArXiv · AI/CL/LG's note
score 4