Spiking nanolasers for all-optical Boltzmann machines

We demonstrate sampling from Boltzmann distributions at 200 MHz in simulations of spiking nanolasers. We then use a network of 144 such lasers to carry out Bayesian inference on a small set of images.

Authors

Mihai A. Petrovici (ORCID: https://orcid.org/0000-0003-2632-0427)
И. В. Бойков (ORCID: https://orcid.org/0000-0002-6980-933X)
A. De Rossi

Institutions

University of Bern (CH)
Thales (France) (FR)

Publication Details

Published: 2024-11-10
DOI: https://doi.org/10.1109/ipc60965.2024.10799532
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Spiking nanolasers for all-optical Boltzmann machines

Mihai A. Petrovici, И. В. Бойков, A. De Rossi

Nov 10, 2024

Neural Networks and Reservoir Computing

article

Spiking nanolasers for all-optical Boltzmann machines

Mihai A. Petrovici, И. В. Бойков, A. De Rossi

November 10, 2024article en

Abstract

University of Bern (CH), Thales (France) (FR)

Affordable and clean energy

Openalex Percentile: Top 78%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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Spiking nanolasers for all-optical Boltzmann machines — Mihai A. Petrovici, И. В. Бойков, et al. · (2024) | TGRS Research Map | TGRS