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Dharia

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2026-07-21 / arxiv-researcherDifferentiable Logic Gate Networks Run EEG Classification on Edge Devices Without Floating PointShyamal Y. Dharia, Stephen D. Smith and Camilo E. Valderrama (arXiv 2607.18149) replace conventional floating-point neural networks with differentiable logic gate networks for real-time EEG classification, targeting the arithmetic bottleneck on microcontroller-class hardware. Logic gate networks compile to boolean circuits, so inference is effectively free relative to matmul. This is a concrete data point for the broader thesis that edge inference will not be quantized transformers.

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replace floating-point networks

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