TinyOL: TinyML with Online-Learning on Microcontrollers
Open accessAbstract
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 setting 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.
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Authors in [140] proposed a Block based Binary Shallow Echo State Network (BBS-ESN) model which is a deeply quantized anomaly detector of oil leaks that happen in wind turbines.
“The authors in [140] proposed a Block based Binary Shallow Echo State Network (BBS-ESN) model which is a deeply quantized anomaly detector of oil leaks that happen in the wind turbines.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [140]
Cites
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TinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
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“Similarly, a near real-time training concept is proposed in [24] for computer vision tasks on NVIDIA Jetson.”
BackgroundDeep Active Learning for Anomaly Detection
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Incremental On-Device Tiny Machine Learning
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A review on TinyML: State-of-the-art and prospects
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A Comprehensive Survey on TinyML
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A Novel Online Machine Learning Approach for Real-Time Condition Monitoring of Rotating Machines
Mostafavi, A., & Sadighi, A. (2021) · 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM) · doi:10.1109/icrom54204.2021.9663495 · Cited by 20 · see citing works
- Authors
- Ren, H., Anicic, D., & Runkler, T. A.
- Affiliations
- Siemens (Germany) (DE)
- Venue
- 2021 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2021-07-18
- Pages
- 1-8
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W3199421948
- Semantic Scholar
- 5c11eb794fe2523d7e2a3f0e2f1caa65f922fab2
- Cited by
- 185
- References
- 20
- Citations by year
- 2026: 19 · 2025: 40 · 2024: 52 · 2023: 33 · 2022: 27 · 2021: 14
- FWCI
- 29.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- TinyML, online learning, microcontroller, on-device learning, incremental learning, data streams, Internet of Things, autoencoder, resource-constrained devices
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