Welcome!

SciTinyML: Scientific Use of Machine Learning on Low-Power Devices will be run regionally for 2022 with seperate workshops for Africa, Asia, and Latin America. The Africa and Asia Workshops were taught in English while the Latam Workshop was taught in Spanish with select presentations in English.

To view the materials and videos from past years SciTinyML workshops and TinyML4D seminars please visit the TinyML4D Past Events page.

SciTinyML is an ICTP Virtual Meeting supported by the TinyML4D Academic Network and open to all.

TinyML is a subfield of Machine Learning focused on developing models that can be executed on small, realtime, low-power, and low-cost embedded devices. This allows for new scientific applications to be developed at an extremely low cost and at large scale.

The TinyML process starts with collecting data from IoT devices, then training the collected dataset to extract knowledge patterns; these patterns are then packaged into a TinyML model that considers the target microprocessor’s limited resources such as memory and processing power. The resulting model is then deployed on embedded devices where it is used to evaluate new sensor data in real-time. Typically, power requirements are in the mW range and below which enables a variety of use-cases targeting battery operated devices. TinyML represents a collaborative effort between the embedded power systems and Machine Learning communities, which traditionally have operated independently.

Workshop Topics:

ML general concepts
Introduction to TinyML
Getting started with the TinyML training kit
Examples of TinyML applications
Scientific Applications of ML

Questions?

Contact edu@tinyml.org with any questions regarding this workshop.

Supporters

We would like to thank Harvard SEAS, the tinyML Foundation, and ICTP for their continued leadership and support of all of our TinyML educational content!