# TinyOL: TinyML with Online-Learning on Microcontrollers

arXiv: https://arxiv.org/abs/2103.08295 · HTML: https://mlsanity.com/paper/2103.08295/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS](https://mlsanity.com/paper/1804.05554/) (2018)
- [Deep Active Learning for Anomaly Detection](https://mlsanity.com/paper/1805.09411/) (2018)
- [Training on the Edge: The why and the how](https://mlsanity.com/paper/1903.03051/) (2019)
- [The Case for Retraining of ML Models for IoT Device Identification at the Edge](https://mlsanity.com/paper/2011.08605/) (2020)
- [Enabling Incremental Knowledge Transfer for Object Detection at the Edge](https://mlsanity.com/paper/2004.05746/) (2020)

## Built on by (most cited first, 30 of 65)

- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [Tiny Machine Learning: Progress and Futures](https://mlsanity.com/paper/2403.19076/) (2024)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [On-device Training: A First Overview on Existing Systems](https://mlsanity.com/paper/2212.00824/) (2022)
- [TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge](https://mlsanity.com/paper/2307.09988/) (2023)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems](https://mlsanity.com/paper/2403.10549/) (2024)
- [Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers](https://mlsanity.com/paper/2305.19167/) (2023)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [Tiny Machine Learning for Concept Drift](https://mlsanity.com/paper/2107.14759/) (2021)
- [QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version](https://mlsanity.com/paper/2404.13990/) (2024)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT](https://mlsanity.com/paper/2105.03371/) (2021)
- [How to Manage Tiny Machine Learning at Scale: An Industrial Perspective](https://mlsanity.com/paper/2202.09113/) (2022)
- [Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing](https://mlsanity.com/paper/2403.04759/) (2024)
- [On-device Online Learning and Semantic Management of TinyML Systems](https://mlsanity.com/paper/2405.07601/) (2024)
- [TinySV: Speaker Verification in TinyML with On-device Learning](https://mlsanity.com/paper/2406.01655/) (2024)
- [On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers](https://mlsanity.com/paper/2407.10734/) (2024)
- [A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption](https://mlsanity.com/paper/2409.07114/) (2024)
- [Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach](https://mlsanity.com/paper/2411.05873/) (2024)
- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors](https://mlsanity.com/paper/2209.00591/) (2022)
- [RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration](https://mlsanity.com/paper/2301.03904/) (2023)
- [TinyReptile: TinyML with Federated Meta-Learning](https://mlsanity.com/paper/2304.05201/) (2023)
- [TinyProp -- Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning](https://mlsanity.com/paper/2308.09201/) (2023)
- [Enabling On-device Continual Learning with Binary Neural Networks](https://mlsanity.com/paper/2401.09916/) (2024)
- [On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors](https://mlsanity.com/paper/2403.04071/) (2024)

## Abstract (arXiv)

Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on power, memory, and computation, TinyML has achieved significant advancement in the last few years. However, the current TinyML solutions are based on batch/offline settings and support only the neural network's inference on MCUs. The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs. This results in a static model, hard to adapt to new data, and impossible to adjust for different scenarios, which impedes the flexibility of the Internet of Things (IoT). To address these problems, we propose a novel system called TinyOL (TinyML with Online-Learning), which enables incremental on-device training on streaming data. TinyOL is based on the concept of online learning and is suitable for constrained IoT devices. We experiment TinyOL under supervised and unsupervised setups using an autoencoder neural network. Finally, we report the performance of the proposed solution and show its effectiveness and feasibility.