SciTinyML-21 is an ICTP Virtual Meeting supported by the TinyML4D Academic Network and open to all. This program is a collaboration led by the Abdus Salam International Centre for Theoretical Physics (ICTP), the Harvard John A. Paulson School of Engineering and Applied Sciences, and TinyMLedu with 216 participants from 48 countries.
Embedded machine learning (tinyML) enables machine learning technologies to perform on-device analytics of sensor data at extremely low power. This allows for new scientific applications to be developed at an extremely low cost and at large scale. In recent years, hardware advancements have made it possible for microcontrollers to perform calculations much faster. Improved hardware has made it easier for developers to build programs on these devices. Perhaps the most important trend for scientists has been the rise of embedded machine learning, or tinyML. Between hardware advancements and the tinyML community’s recent innovations in machine learning, it is now possible to run increasingly complex deep learning models directly on microcontrollers. tinyML represents a collaborative eﬀort between the embedded power systems and machine learning communities, which traditionally have operated independently.
Workshop topics include an introduction to embedded ML (tinyML), hands-on examples of tinyML applications, and acientific applications of ML.