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SElf-Adaptive and Automated LEARNing Framework for Smart Sensors

Objective

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.

Coordinator

KATHOLIEKE UNIVERSITEIT LEUVEN
Net EU contribution
€ 175 920,00
Address
Oude Markt 13
3000 Leuven
Belgium

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Region
Vlaams Gewest Prov. Vlaams-Brabant Arr. Leuven
Activity type
Higher or Secondary Education Establishments
Other funding
No data

Partners (1)

GREENWAVES TECHNOLOGIES
France
Net EU contribution
€ 0,00
Address
28 Cours Jean Jaures
38000 Grenoble

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Auvergne-Rhône-Alpes Rhône-Alpes Isère
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Other funding
No data