no code implementations • 12 Mar 2021 • Nadezhda Semenova, Laurent Larger, Daniel Brunner
Here, we determine for the first time the propagation of noise in deep neural networks comprising noisy nonlinear neurons in trained fully connected layers.
no code implementations • 27 Mar 2020 • Louis Andreoli, Xavier Porte, Stéphane Chrétien, Maxime Jacquot, Laurent Larger, Daniel Brunner
A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate.
no code implementations • 17 Dec 2019 • Johnny Moughames, Xavier Porte, Michael Thiel, Gwenn Ulliac, Maxime Jacquot, Laurent Larger, Muamer Kadic, Daniel Brunner
Photonic waveguides are prime candidates for integrated and parallel photonic interconnects.
no code implementations • 23 Jul 2019 • Xavier Porte, Louis Andreoli, Maxime Jacquot, Laurent Larger, Daniel Brunner
However, important questions regarding impact of reservoir size and learning routines on the convergence-speed during learning remain unaddressed.
no code implementations • 21 Jul 2019 • Nadezhda Semenova, Xavier Porte, Louis Andreoli, Maxime Jacquot, Laurent Larger, Daniel Brunner
The system under study consists of noisy linear nodes, and we investigate the signal-to-noise ratio at the network's outputs which is the upper limit to such a system's computing accuracy.
no code implementations • 4 May 2018 • Bogdan Penkovsky, Laurent Larger, Daniel Brunner
In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort.
no code implementations • 14 Nov 2017 • Julian Bueno, Sheler Maktoobi, Luc Froehly, Ingo Fischer, Maxime Jacquot, Laurent Larger, Daniel Brunner
Realizing photonic Neural Networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware was lacking so far.
no code implementations • 13 Oct 2015 • Lyudmila Grigoryeva, Julie Henriques, Laurent Larger, Juan-Pablo Ortega
This paper addresses the reservoir design problem in the context of delay-based reservoir computers for multidimensional input signals, parallel architectures, and real-time multitasking.