New publication: Training a multilayer dynamical spintronic network with standard ML tools

We propose to implement a recurrent neural network in hardware using spintronic oscillators as dynamical neurons. Using numerical simulations, we build a multilayer network and demonstrate that we can use back-propagation through time and standard machine-learning tools to train this network. The wide range of time scales that the network can handle enables high accuracy on time-series classification.

https://link.aps.org/doi/10.1103/PhysRevApplied.23.034051

https://arxiv.org/abs/2408.02835

Images are converted to time-series then classified by the spintronic network. Wide time scale adaptation is provided by the device physics.

Baptiste Carles prepares his talk for Aarhus University

Our group meetings are very interdisciplinary. Some of us focus on machine learning, working at their desk mostly, while others favor sample fabrication in the clean room. So we try to be as pedagogical as we can. Here Baptiste trains for his talk on Bosonic reservoir computing.

Baptiste obtained very cool experimental results, showing how to induce and exploit non-linearity in quantum reservoirs. He will present his work at this workshop in Denmark.

Article under preparation!