tinyML Foundation

The community for ultra-low power machine learning at the edge.

Join us for the tinyML Summit 2023 – in person – March 27-29, 2023 in San Francisco

About us


Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.

Upcoming Events6



February 21, 2023

tinyML Deployment Working Group – White Paper

The tinyML Foundation Deployment Working Group is pleased to publish our first white paper exploring the challenges and solutions for deploying ultra-low power machine learning (ML) at the end of the cloud!

January 18, 2023

Summit 2023 Awards

The tinyML Summit organizers are pleased to continue their Best Product of the Year and Best Innovation of the Year nominations. However, for 2023, more categories have been added to each.

January 23, 2023

EMEA 2023 Call for Presentations

The tinyML EMEA Innovation Forum is accelerating the adoption of tiny machine learning across the region by connecting the efforts of the private sector with those of academia in pushing the boundaries of machine learning and artificial intelligence on ultra-low powered devices.

Industry News

    January 29, 2022

    TinyML unlocks new possibilities for sustainable development technologies

    In this article, we take a look at two tinyML projects that have the potential to make contributions towards sustainable development goals. While the first project is about revolutionising precision farming, the second one aims to create a network of low-cost sensors for mapping carbon emissions.

    January 31, 2022

    TinyML is bringing deep learning models to microcontrollers

    Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill…