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 µNPUs 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 the size (∼ 370KB with 8-bit quantization). We also profile its component-wise latency and resource consumption on both the MAX78000 and HX6538 WE2 µNPUs, demonstrating its viability for signal analysis on constrained, battery-powered hardware. We release our model(s) and training framework at: https://github.com/j0shmillar/physiolite.

Efficient on-device neural network (NN) inference offers predictable latency, improved privacy and reliability, and lower operating costs for vendors than cloud-based inference. This has sparked recent development of microcontroller-scale NN accelerators, also known as neural processing units (μNPUs), designed specifically for ultra-low-power applications.

The Kunming-Montreal Global Biodiversity Framework sets ambitious targets for 2023, including halting human-induced species extinction. Achieving these requires comprehensive data on global biodiversity patterns, which can only be gathered through in-situ distributed sensor networks. However, these multi-device networks are constrained by battery lifetimes, must gather rich data from power-hungry sensors, and yet need to be deployed in remote environments for long periods. This note introduces a prototype multi-sensor device, and outlines how collaborative event-driven scheduling could improve network lifetime and reliability.

The Kunming-Montreal Global Biodiversity Framework sets ambitious targets for 2023, including halting human-induced species extinction. Achieving these requires comprehensive data on global biodiversity patterns, which can only be gathered through in-situ distributed sensor networks. However, these multi-device networks are constrained by battery lifetimes, must gather rich data from power-hungry sensors, and yet need to be deployed in remote environments for long periods. This note introduces a prototype multi-sensor device, and outlines how embedded scheduling could be used for extending sensor lifetime and resource-efficiency.