| Description |
PRELIMINARY PROGRAMME as of 22.04.2022 (updated on 26.04.2022: please note changes on Day 3).
The Workshop will run each day from 1:00 PM to 4:00 PM GMT (please check your local time https://greenwichmeantime.com/time-zone/africa/time-zones/). The agenda of the Workshop is also available at tinyml.seas.harvard.edu/SciTinyML-22/africa/, with further links.
DAY 1
 DAY 2  DAY 3  DAY 4  DAY 5  - - - - - - - - - - - - - - - - - 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 applications expired on 15 April 2022 Priority has been given to African participants that are part of the ICTP TinyML Academic Network. Interested candidates from Latin America and from Asia are invited to consider applying for dedicated editions of this online activity, namely: - Latin American Regional Workshop on SciTinyML: Scientific Use of Machine Learning on Low-Power Devices (see: smr.3721) - Asian Regional Workshop on SciTinyML: Scientific Use of Machine Learning on Low-Power Devices (see: smr.3715) |
Material
Poster
African regional workshop on SciTinyML: Scientific Use of Machine Learning on Low-Power Devices | (smr 3708)
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