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.
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.
- ML general concepts
- Introduction to TinyML
- Getting started with the TinyML training kit
- Examples of TinyML applications
- Scientific Applications of ML
Contact firstname.lastname@example.org with any questions regarding this workshop.
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!