1.2k citations · 8.7k across the 113 of their papers we have counts for
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Convolutional Neural Networks with Radio-Frequency Spintronic Nano-Devices
Nathan Leroux, Arnaud De Riz, Dédalo Sanz-Hernández +3
Convolutional neural networks are state-of-the-art and ubiquitous in modern signal processing and machine vision. Nowadays, hardware solutions based on emerging nanodevices are des…
Easy-plane spin Hall nano-oscillators as spiking neurons for neuromorphic computing
Danijela Marković, Matthew W. Daniels, Pankaj Sethi +3
We show analytically using a macrospin approximation that easy-plane spin Hall nano-oscillators excited by a spin-current polarized perpendicularly to the easy-plane have phase dyn…
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations
Xing Chen, Flavio Abreu Araujo, Mathieu Riou +6
Deep learning has an increasing impact to assist research, allowing, for example, the discovery of novel materials. Until now, however, these artificial intelligence techniques hav…
Mesoscopic magnetic systems: from fundamental properties to devices
Laura J. Heyderman, Julie Grollier, Christopher H. Marrows +4
Here we review various themes of current research within mesoscopic magnetic systems.
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Training Dynamical Binary Neural Networks with Equilibrium Propagation
Jérémie Laydevant, Maxence Ernoult, Damien Querlioz +1
Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, thanks to the local updates of weights given by the internal dynamics of th…