RadioSpin
DEEP OSCILLATORY NEURAL NETWORKS COMPUTING AND LEARNING THROUGH THE DYNAMICS OF RF NEURONS INTERCONNECTED BY RF SPINTRONIC SYNAPSES
Developing artificial neural networks that can learn, process and 'think' as the brain does is a sort of Holy Grail with virtually limitless applications. Deep learning paradigms exploiting multilayer hierarchical artificial neural networks mimicking the brain's structure can also mimic the brain's ability to learn by example. They have achieved tremendous success, even exceeding human performance in some cases. The EU-funded RadioSpin project plans to demonstrate deep learning networks processing radiofrequency (RF) signals and learning at speeds up to 10 million times faster than a human brain. Benchmarking applications will target mammography and IoT RF fingerprinting.
Total Eligible Budget
3,872,942.50 €
INL Eligible Budget
726,562.50 €
Total Funding
3,872,942.50 €
INL Funding
726,562.50 €
Start Date
01-01-2021
End Date
28-02-2025
Website
https://www.radiospin.eu/
Type of action
RIA Research and Innovation action
Grant Agreement Id
101017098
Programme
FET – Future Emerging Technologies
Funding Framework
HORIZON 2020
INL Role
Partner
Approval Date
24-09-2020
Main Objective