DEEP OSCILLATORY NEURAL NETWORKS COMPUTING AND LEARNING THROUGH THE DYNAMICS OF RF NEURONS INTERCONNECTED BY RF SPINTRONIC SYNAPSES

The goal of RadioSpin is to build a hardware neural network that computes using neural dynamics as in the brain, has a deep layered architecture as in the neocortex, but runs and learns faster, by seven orders of magnitude.

For this purpose, we will use ultrafast radio-frequency (RF) oscillators to imitate the rich, reconfigurable dynamics of biological neurons.

Within the RadioSpin project, we will develop a new breed of nanosynapses, based on spintronics technology, that directly process the RF signals sent by neurons and interconnects them layer-wise.

We will demonstrate and benchmark our concept by building a lab-scale prototype that co-integrates for the first time CMOS RF neurons with spintronic RF synapses.

We will develop brain-inspired algorithms harnessing oscillations, synchrony and edge-of-chaos for computing and show that they can run on RadioSpin deep network RF technology.

Finally, we will benchmark RadioSpin technology for biomedical and RF fingerprinting applications where fast and low energy consumption classification of RF signals are key.

To achieve its ambitious goals RadioSpin brings together frontier researchers along the entire chain of neuromorphic engineering, from material science (spintronic nanodevices), physics (non-linear dynamics), electronics (RF CMOS design), computer science (artificial intelligence algorithms), and microwave signal processing.

Two innovative companies bring real-life use-cases (microwave mammography and IoT RF fingerprinting).

The scientific experts are further complemented by experts in the field of innovation, commercial deployment and IP monetisation, as well as communication and public engagement.