Projektbeschreibung
Intelligente Sensoren als Beitrag zu einem grüneren und digitaleren Europa
Die EU durchläuft einen ehrgeizigen grünen und digitalen Wandel, der durch intelligente Sensortechnologie untermauert wird. Intelligente Sensoren, die vor allem in den Bereichen Luft- und Raumfahrt, Verteidigung, Industrie und Landwirtschaft eingesetzt werden, dürften im neuen grünen und digitalen Zeitalter Europas ungeachtet aller Herausforderungen eine entscheidende Rolle einnehmen. So ist es zwar möglich, Informationen aus Sensordaten zu extrahieren, doch reicht dies nicht aus, um Lösungen für Verbrauchs- und Industrieanwendungen zu gewährleisten. In diesem Zusammenhang wird das EU-finanzierte Projekt SEA2Learn energieeffiziente Echtzeit-Mechanismen entwickeln, um die Inferenzfähigkeiten von ressourcenbeschränkten intelligenten Sensoren auf der Grundlage von Umgebungsreizen anzupassen.
Ziel
Smart Sensors are key components for the upcoming Green and Digital European era. Recently, novel emerging electronics components – such as high energy-efficient many-core application processors featuring a power consumption of few tens of mWs – have enabled high-accurate on-device inference capabilities, i.e. Deep Learning inference, to extract high-level information from sensor data. However, this technology improvement is not sufficient to ensure robust solutions suitable for consumer and industrial applications. The main issue comes from the wide variety in real-world test conditions and, consequently, the lack at design-time of representative (labelled) sensor data, needed to train DL inference networks. For this reason, the currently used “train-once-and-deploy-everywhere” design process for edge intelligence has proved to be weak, even after an endless cyclic procedure involving data collection, model training and in-field testing.
This limitation is addressed by the SEA2Learn project by developing energy-efficient and real-time mechanisms to adapt the inference capabilities of resource-constrained smart sensors based on the stimulus from the surrounding environment. The proposed strategy, which is unprecedent in this domain, aims at placing in the same training loop multiple smart sensor nodes that interact with a Learning Agent. The latter will leverage a new class of lightweight methods belonging to the Continual Learning (CL) domain operating on unlabelled multi-sensor data. Thanks to the envisioned SEA2Learn framework, the embedded intelligence can adapt over time based on real-world data, making the design process more robust and 10-100x faster than today. To realize this vision, the fellow’s expertise in HW/SW design for embedded machine learning will be complemented by the Continual Learning knowledge of the hosting research group at KU Leuven and enriched by a tight collaboration with an SME that manufactures IoT platforms for edge computing.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencesinternetinternet of things
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorssmart sensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsKoordinator
3000 Leuven
Belgien