Probabilistic photonic computing with chaotic light
Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making...
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
01 December 2024
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| In: |
Nature Communications
Year: 2024, Jahrgang: 15, Pages: 1-10 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-024-54931-6 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-024-54931-6 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-024-54931-6 
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| Verfasserangaben: | Frank Brückerhoff-Plückelmann, Hendrik Borras, Bernhard Klein, Akhil Varri, Marlon Becker, Jelle Dijkstra, Martin Brückerhoff, C. David Wright, Martin Salinga, Harish Bhaskaran, Benjamin Risse, Holger Fröning & Wolfram Pernice |
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| 245 | 1 | 0 | |a Probabilistic photonic computing with chaotic light |c Frank Brückerhoff-Plückelmann, Hendrik Borras, Bernhard Klein, Akhil Varri, Marlon Becker, Jelle Dijkstra, Martin Brückerhoff, C. David Wright, Martin Salinga, Harish Bhaskaran, Benjamin Risse, Holger Fröning & Wolfram Pernice |
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| 520 | |a Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as “point estimates” that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling. | ||
| 650 | 4 | |a Nanophotonics and plasmonics | |
| 650 | 4 | |a Photonic devices | |
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| 700 | 1 | |a Klein, Bernhard |e VerfasserIn |0 (DE-588)1341625443 |0 (DE-627)1902317734 |4 aut | |
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| 700 | 1 | |a Brückerhoff, Martin |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wright, C. David |e VerfasserIn |4 aut | |
| 700 | 1 | |a Salinga, Martin |e VerfasserIn |4 aut | |
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