TinyML is a subfield of Machine Learning focused on developing models that can be executed on small, real-time, 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.

TOPICS

• ML general concepts
• Introduction to TinyML
• Getting started with the TinyML training kit
• Examples of TinyML applications
• Scientific Applications of ML DEADLINE FOR APPLICATION IS OVER.

Priority given to Asian candidates that are part of the ICTP TinyML Academic Network.

Interested candidates from other regions:
- Potential candidates from Latin America are invited to consider applying for a dedicated edition of this online activity: Latin American Regional Workshop on SciTinyML: Scientific Use of Machine Learning on Low-Power Devices (see: smr.3721)
- The African Regional Workshop on SciTinyML: Scientific Use of Machine Learning on Low-Power Devices has been held from 25 to 29 April 2022  (see: smr.3708)