TL;DR
PhysioLite is a lightweight, NPU-compatible model architecture for ECG and EMG analysis that achieves near-state-of-the-art performance on constrained hardware, enabling real-time signal processing on microcontrollers.
Contribution
The paper introduces PhysioLite, a novel, hardware-aware model architecture and training framework optimized for low-power microcontroller units for physiological signal analysis.
Findings
PhysioLite achieves comparable accuracy to large Transformer models on ECG and EMG benchmarks.
The model size is less than 10% of state-of-the-art Transformer models, around 370KB with 8-bit quantization.
Latency and resource profiling demonstrate PhysioLite's suitability for real-time processing on microcontrollers.
Abstract
The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable hardware, supporting near-real-time, offline, and privacy-preserving inference. Yet physiological signal analysis has remained infeasible on such hardware; recent Transformer-based models show state-of-the-art performance but are prohibitively large for resource- and power-constrained hardware and incompatible with NPUs due to their dynamic attention operations. We introduce PhysioLite, a lightweight, NPU-compatible model architecture and training framework for ECG/EMG signal analysis. Using learnable wavelet filter banks, CPU-offloaded positional encoding, and hardware-aware layer design, PhysioLite reaches performance comparable to state-of-the-art Transformer-based foundation models on ECG and EMG benchmarks, while being <10% of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
