5 citations · 5 across the 7 of their papers we have counts for
7 papers
Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal
M. Menshawy, D. Sanz-Hernández, L. Mazza +7
Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we demonstrate remote progr…
Convolutions with Radio-Frequency Spin-Diodes
Erwann Plouet, Hanuman Singh, Pankaj Sethi +3
The classification of radio-frequency (RF) signals is crucial for applications in robotics, traffic control, and medical devices. Spintronic devices, which respond to RF signals vi…
Training a multilayer dynamical spintronic network with standard machine learning tools to perform time series classification
Erwan Plouet, Dédalo Sanz-Hernández, Aymeric Vecchiola +2
The ability to process time-series at low energy cost is critical for many applications. Recurrent neural network, which can perform such tasks, are computationally expensive when…
Spiking Dynamics in Dual Free Layer Perpendicular Magnetic Tunnel Junctions
Louis Farcis, Bruno Teixeira, Philippe Talatchian +8
Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short and long term memory, non-linear fast r…
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…