TinyML
TinyML (short for tiny machine learning) is an area of machine learning that focuses on deploying and running models on low-power, resource-constrained embedded systems such as microcontrollers and edge devices.[1][2][3][4]
TinyML supports on-device inference with low latency and minimal reliance on cloud connectivity, which makes it suitable for applications in the Internet of Things (IoT), wearable devices, and real-time systems.[5]
History
The idea of running machine learning models on embedded systems has gained traction in the late 2010s, as model compression, quantization, and efficient neural network architectures progressed.[6]
The term TinyML was popularized in 2019 with the publication of the book TinyML by Pete Warden and Daniel Situnayake and the creation of the TinyML Foundation.[1]
See also
Further reading
Warden, Pete; Situnayake, Daniel (2020). TinyML: machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers (1st ed.). Bejing Boston Farnham Sebastopol Tokyo: O'Reilly. ISBN 978-1-4920-5204-3.
References
- ↑ 1.0 1.1 Warden, Pete; Situnayake, Daniel (2019). TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers. O'Reilly Media..
- ↑ "What is TinyML? Tiny Machine Learning" (in en). 2024-01-04. https://www.geeksforgeeks.org/machine-learning/what-is-tinyml-tiny-machine-learning/.
- ↑ Prakash, Shvetank; Njor, Emil; Banbury, Colby; Stewart, Matthew; Janapa, Vijay (2024-05-06). "TinyML: Why the Future of Machine Learning is Tiny and Bright" (in en-US). https://www.sigarch.org/tiny-machine-learning-the-future-of-ml-is-tiny-and-bright/.
- ↑ Han, Hui; Trimi, Silvana; Lee, Sang M. (2026-03-01). "Tiny Machine Learning (TinyML): Research trends and future application opportunities". Array 29. doi:10.1016/j.array.2025.100674. ISSN 2590-0056. https://www.sciencedirect.com/science/article/pii/S2590005625003017.
- ↑ Davidson, Joe (2021). "Enabling TinyML for IoT Applications". IEEE Internet of Things Journal.
- ↑ Gupta, Suyog; Agrawal, Ankur; Gopalakrishnan, Kailash; Narayanan, Pritish (2015). "Deep Learning with Limited Numerical Precision". Proceedings of the 32nd International Conference on Machine Learning 37: 1737–1746. https://proceedings.mlr.press/v37/gupta15.html. Retrieved 30 April 2026.
 |  |
