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.1234
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
Further reading
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
References
- 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". GeeksforGeeks. 4 January 2024. Retrieved 27 April 2026.
- Prakash, Shvetank; Njor, Emil; Banbury, Colby; Stewart, Matthew; Janapa, Vijay (6 May 2024). "TinyML: Why the Future of Machine Learning is Tiny and Bright". SIGARCH. Retrieved 27 April 2026.
- Han, Hui; Trimi, Silvana; Lee, Sang M. (1 March 2026). "Tiny Machine Learning (TinyML): Research trends and future application opportunities". Array. 29 100674. doi:10.1016/j.array.2025.100674. ISSN 2590-0056.
- 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. Retrieved 30 April 2026.