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Multilayer spintronic neural networks with radio-frequency connections
Andrew Ross, Nathan Leroux, Arnaud de Riz +18
Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. Thes…
Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
Nathan Leroux, Danijela Marković, Dédalo Sanz-Hernández +8
Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to healt…
Quantum reservoir neural network implementation on coherently coupled quantum oscillators
Julien Dudas, Baptiste Carles, Erwan Plouet +3
Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and quantum input data. However, current a…
Characterization and modeling of spiking and bursting in experimental NbOx neuron
Marie Drouhin, Shuai Li, Matthieu Grelier +7
Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used…
Coherently coupled quantum oscillators for quantum reservoir computing
Julien Dudas, Julie Grollier, Danijela Marković
We analyze the properties of a quantum system composed of two coherently coupled quantum oscillators and show through simulations that it fulfills the two properties required for r…
Quantum materials for energy-efficient neuromorphic computing
Axel Hoffmann, Shriram Ramanathan, Julie Grollier +16
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum mate…