Abstract
Artificial Neural Networks (ANNs) have become a ubiquitous technology; indeed, their flexibility allows them to excel in a wide range of tasks, ranging from medical diagnosis to language models. Contrary to classical algorithms, these networks process information in parallel. Photonics, in particular, shows great promise as a platform for implementing ANNs in terms of scalability, speed, energy efficiency and parallel information processing [1]. In [2], we physically implemented the first fully autonomous PNN (photonic neural network), using spatially multiplexed modes of an injection locked large area vertical cavity surface emitting laser (LA-VCSEL). All components of our PNN, including learning are fully realized in hardware using off-the-shelf, commercially available, low energy consumption components, while still achieving >98% accuracy in 6-bit header recognition tasks.
© 2023 IEEE
PDF ArticleMore Like This
Anas Skalli, Mirko Goldmann, Xavier Porte, Nasibeh Haghighi, Stephan Reitzenstein, James A. Lott, and Daniel Brunner
Tu3B.2 Nonlinear Optics (NLO) 2023
Elizabeth Robertson, Anas Skalli, Romain Lance, Xavier Porte, Janik Wolters, and Daniel Brunner
jsiii_p_1 European Quantum Electronics Conference (EQEC) 2023
Adria Grabulosa, Anas Skalli, Johnny Moughames, Xavier Porte, and Daniel Brunner
jsiii_6_1 European Quantum Electronics Conference (EQEC) 2023