18 citations · 37 across the 12 of their papers we have counts for
12 papers
Physical Reservoir Signal Acquisition for Sub-Nyquist Waveform Reconstruction
Yuito Ito, Anas Skalli, Tetsuya Asai +1
Physical reservoir computing has traditionally exploited the dynamics of physical systems for computation, enabling tasks such as inference, classification, and prediction. Here, w…
Power law scaling for classification accuracy in physical neural networks
Andrei V. Ermolaev, Mathilde Hary, Anas Skalli +7
Physical neural networks (PNNs) harness the intrinsic complexity of physical systems to perform neural computation, potentially at speeds and energy efficiencies inaccessible to co…
The thin line for optical neural networks towards broad practical relevance
Anas Skalli, Daniel Brunner
Optical neural networks promise unmatched efficiency, bandwidth, and latency, critical benefits as demand for neural network hardware surges. However, their practical value for gen…
Model-free front-to-end training of a large high performance laser neural network
Anas Skalli, Satoshi Sunada, Mirko Goldmann +5
Artificial neural networks (ANNs), have become ubiquitous and revolutionized many applications ranging from computer vision to medical diagnoses. However, they offer a fundamentall…
Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine
Andrei V. Ermolaev, Mathilde Hary, Lev Leybov +5
We report a generalized nonlinear Schrödinger equation simulation model of an extreme learning machine (ELM) based on optical fiber propagation. Using the MNIST handwritten digit d…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity
Ria Talukder, Anas Skalli, Xavier Porte +2
Spiking neural networks are neuromorphic systems that emulate certain aspects of biological neurons, offering potential advantages in energy efficiency and speed by for example lev…
Annealing-inspired training of an optical neural network with ternary weights
Anas Skalli, Mirko Goldmann, Nasibeh Haghighi +3
Artificial neural networks (ANNs) represent a fundamentally connectionnist and distributed approach to computing, and as such they differ from classical computers that utilize the…
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…
Computational metrics and parameters of an injection-locked large area semiconductor laser for neural network computing
Anas Skalli, Xavier Porte, Nasibeh Haghighi +3
Artificial neural networks have become a staple computing technique in many fields. Yet, they present fundamental differences with classical computing hardware in the way they proc…
Photonic neuromorphic computing using vertical cavity semiconductor lasers
Anas Skalli, Joshua Robertson, Dafydd Owen-Newns +5
Photonic realizations of neural network computing hardware are a promising approach to enable future scalability of neuromorphic computing. In this review we provide an overview on…
A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser
Xavier Porte, Anas Skalli, Nasibeh Haghighi +3
Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing in a number of fundamental aspect…