20 citations · 42 across the 9 of their papers we have counts for
9 papers
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…
Experimental reservoir computing with diffractively coupled VCSELs
Moritz Pflüger, Daniel Brunner, Tobias Heuser +3
We present experiments on reservoir computing (RC) using a network of vertical-cavity surface-emitting lasers (VCSELs) that we diffractively couple via an external cavity. Our opti…
Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
Nadezhda Semenova, Daniel Brunner
In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and comple…
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…