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Communication Dans Un Congrès Année : 2019

Scaling laws and topology-properties of Boolean greedy learning in photonic neural networks

Résumé

Since recent years artificial intelligence and more particularly neural networks play a major role in our technological societies. Nevertheless, neural networks still remain emulated by traditional computers, resulting in challenging problems such as parallelization, energy efficiency and potentially speed. A change of paradigm is desirable but implementating neural networks in hardware is a non-trivial challenge. One highly promising avenue are optical neural networks [1], potentially avoiding parallelization bottlenecks.
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Dates et versions

hal-02382645 , version 1 (27-11-2019)

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  • HAL Id : hal-02382645 , version 1

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Louis Andreoli, Javier Porte, Maxime Jacquot, Laurent Larger, Daniel Brunner. Scaling laws and topology-properties of Boolean greedy learning in photonic neural networks. Conference on Lasers and Electro-Optics/Europe and the European Quantum Electronics Conference, Jun 2019, Munich, Germany. ⟨hal-02382645⟩
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