Towards Real-Time ECG and EMG Modeling on $μ$ NPUs

2026-04-20

AI Summary

This work addresses the computational and memory bottlenecks of deploying high-performance Transformer models for electrocardiogram (ECG) and electromyogram (EMG) analysis on resource- and power-constrained micro neural processing units (μNPUs). To this end, the authors propose PhysioLite—a lightweight, hardware-aware model architecture and training framework that integrates learnable wavelet filter banks, CPU-offloaded positional encoding, μNPU-optimized network layers, and 8-bit quantization. This approach achieves state-of-the-art accuracy on ECG and EMG tasks while reducing model size to approximately 370 KB—less than 10% of the baseline—and demonstrates efficient real-time inference with low latency and power consumption. Notably, PhysioLite is the first to enable effective physiological signal modeling on real-world μNPU platforms such as the MAX78000 and HX6538 WE2.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 $μ$ 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 the size ($\sim$370KB with 8-bit quantization). We also profile its component-wise latency and resource consumption on both the MAX78000 and HX6538 WE2 $μ$ NPUs, 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.

Problem

• ECG
• EMG
• μNPU
• real-time
• physiological signal analysis

Innovation

• PhysioLite
• μNPU
• learnable wavelet filter banks
• hardware-aware design
• ECG/EMG modeling