5 papers
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…
High Clockrate Free-space Optical In-Memory Computing
Yuanhao Liang, James Wang, Kaiwen Xue +12
The ability to process and act on data in real time is increasingly critical for applications ranging from autonomous vehicles, three-dimensional environmental sensing and remote r…
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…
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…