SciTinyML: Scientific Use of Machine Learning on Low-Power Devices will be run virtually from October 18-22, 2021.
The Zoom link was sent out to all registered attendees. Please check your email and/or sign up for our workshop.
SciTinyML is an ICTP Virtual Meeting supported by the TinyML4D Academic Network and open to all.
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.
- Introduction to Embedded ML (tinyML)
- Examples of tinyML applications
- Scientific Applications of ML
|Introduction to Embedded ML (tinyML)
|Vijay Janapa Reddi of Harvard University and Laurence Moroney of Google
|Hands on Embedded ML - Vision and Audio
|Brian Plancher and Mark Mazumder of Harvard University
|Sensors and Ethical Issues for ML and IoT
|Serge Stinckwich and Attlee Gamundani of United Nations University Institute in Macau and Sebastian Büttrich of IT University of Copenhagen
|Hands on Embedded ML - Motion/Anomaly Detection and Scientific Applications
|Marcelo Rovai of UNIFEI and Matthew Stewart of Harvard University
|Academic Network Next Steps and Closing Keynotes
|Marco Zennaro of ICTP, Hal Speed of Robotical, Susan Kennedy of Santa Clara University, and Pete Warden of Google
Contact firstname.lastname@example.org with any questions regarding this workshop.
We would like to thank Harvard SEAS, tinyML Foundation, ICTP, Edge Impulse, Google, and the TensorFlow Lite Micro Team for the continued support of all of our TinyML educational content!