1.2k citations · 8.7k across the 113 of their papers we have counts for
118 papers
Fast simulation of nonlinear deep resistive networks for energy-based computation
Filip Osana, Julie Grollier, Damien Querlioz
Deep resistive networks are electronic energy-based systems in which computation is performed by the steady-state voltages of nonlinear circuits. Nonlinear devices enable expressiv…
Training and synchronizing oscillator networks with Equilibrium Propagation
Théophile Rageau, Julie Grollier
Oscillator networks represent a promising technology for unconventional computing and artificial intelligence. Thus far, these systems have primarily been demonstrated in small-sca…
Reconfigurable classifier based on spin torque driven magnetization switching in electrically connected magnetic tunnel junctions
A. Lopez, D. Costa, T. Bohnert +7
A promising branch of neuromorphic computing aims to perform cognitive operations in hardware leveraging the physics of efficient and well-established nano-devices. In this work, w…
Real Space Imaging of Field-Driven Decision-Making in Nanomagnetic Galton Boards
Hanu Arava, Dedalo Sanz-Hernandez, Julie Grollier +1
A possible spintronic route to hardware implementation for decision making involves injecting a domain wall into a bifurcated magnetic nanostrip resembling a Y-shaped junction. A d…
Training of Physical Neural Networks
Ali Momeni, Babak Rahmani, Benjamin Scellier +25
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research…
Unsupervised End-to-End Training with a Self-Defined Target
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy +2
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phase…