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An In-Ear Sleep Modulation SoC Featuring a CNN-LSTM Accelerator with Long-Kernel Memory Reuse and Runtime Dynamic
Yaqian Xu1, Xinyu Chen1, Yuhan Hou1
1University of Toronto, Toronto, ON, Canada.
Digest of Technical Papers. Symposium on VLSI Technology
|September 11, 2026
Abstract:
This work presents a 65 nm SoC for closed-loop in-ear sleep modulation. The SoC performs low-noise in-ear EEG sensing, CNN-LSTM-based sleep stage classification, and phase-specific auditory stimulation under 1 mW. A dedicated accelerator is developed, featuring a memory reuse technique to efficiently process long kernels required for low-frequency EEG feature extraction. A runtime dynamic quantization is incorporated, reducing model size by 3.88× with only a 0.28% loss in accuracy. A sensitivity of 98.3% is achieved for deep sleep detection, enabling effective sleep modulation.