SOURCE-LINKED INTELLIGENCE

SElf-Adaptive and Automated LEARNing Framework for Smart Sensors

CORDIS · observation · Publication date unknown

ronics 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 strat

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recordType

award

status

CLOSED

region

EU

value

175920

unit

EUR