# A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers

arXiv: https://arxiv.org/abs/2402.10748 · HTML: https://mlsanity.com/paper/2402.10748/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [I-BERT: Integer-only BERT Quantization](https://mlsanity.com/paper/2101.01321/) (2021)
- [DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs](https://mlsanity.com/paper/2008.07127/) (2020)
- [Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition](https://mlsanity.com/paper/2203.12932/) (2022)
- [An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things](https://mlsanity.com/paper/2106.06498/) (2021)

## Built on by (most cited first, 6 of 6)

- [Optimizing the Deployment of Tiny Transformers on Low-Power MCUs](https://mlsanity.com/paper/2404.02945/) (2024)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors](https://mlsanity.com/paper/2407.21453/) (2024)
- [Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs](https://mlsanity.com/paper/2505.17662/) (2025)
- [Event-Based Crossing Dataset (EBCD)](https://mlsanity.com/paper/2503.17499/) (2025)
- [ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification](https://mlsanity.com/paper/2607.09680/) (2026)

## Abstract (arXiv)

Wearable systems for the continuous and real-time monitoring of cardiovascular diseases are becoming widespread and valuable assets in diagnosis and therapy. A promising approach for real-time analysis of the electrocardiographic (ECG) signal and the detection of heart conditions, such as arrhythmia, is represented by the transformer machine learning model. Transformers are powerful models for the classification of time series, although efficient implementation in the wearable domain raises significant design challenges, to combine adequate accuracy and a suitable complexity. In this work, we present a tiny transformer model for the analysis of the ECG signal, requiring only 6k parameters and reaching 98.97% accuracy in the recognition of the 5 most common arrhythmia classes from the MIT-BIH Arrhythmia database, assessed considering 8-bit integer inference as required for efficient execution on low-power microcontroller-based devices. We explored an augmentation-based training approach for improving the robustness against electrode motion artifacts noise, resulting in a worst-case post-deployment performance assessment of 98.36% accuracy. Suitability for wearable monitoring solutions is finally demonstrated through efficient deployment on the parallel ultra-low-power GAP9 processor, where inference execution requires 4.28ms and 0.09mJ.