10 papers
Data-Driven Estimation of the interfacial Dzyaloshinskii-Moriya Interaction with Machine Learning
Davi Rodrigues, Andrea Meo, Ali Hasan +8
Machine learning offers powerful tools to support experimental techniques, particularly for extracting latent features from large datasets. In magnetic materials, accurately estima…
Predicting sampling advantage of stochastic Ising Machines for Quantum Simulations
Rutger J. L. F. Berns, Davi R. Rodrigues, Giovanni Finocchio +1
Stochastic Ising machines, sIMs, are highly promising accelerators for optimization and sampling of computational problems that can be formulated as an Ising model. Here we investi…
Trainable Neuromorphic Spintronic Hardware Via Analog Finite-Difference Gradient Methods
Catarina Pereira, Alex Jenkins, Eleonora Raimondo +8
Spintronic nano-neurons offer a promising route towards energy-efficient, high-performance hardware neural networks thanks to their inherent low-input nonlinear dynamics. However,…
An all-magnonic neuron with tunable fading memory
David Breitbach, Moritz Bechberger, Hanadi Mortada +9
Magnonics offers nanometer-scale wave propagation and strong nonlinearities, making it attractive for neuromorphic applications such as artificial neurons. Yet, magnonic elements w…
Magneto-mechanical reservoir computing combining a two-dimensional network of nonlinear mass-spring resonators with magnetic tunnel junctions
Andrea Grimaldi, Davi R. Rodrigues, Andrea Meo +2
Coupled networks of mass-spring resonators have attracted growing attention across multiple fundamental and applied research directions, including reservoir computing for artificia…
Multi-value Probabilistic Computing with current-controlled Skyrmion Diffusion
Thomas B. Winkler, Yuean Zhou, Grischa Beneke +6
Magnetic systems are highly promising for implementing probabilistic computing paradigms because of the fitting energy scales and conspicuous non-linearities. While conventional bi…