Photonic VCSEL-neuron for spike-rate representation of digital image data

Hejda, Matĕj and Robertson, Joshua and Bueno, Julián and Alanis, Juan Arturo and Hurtado, Antonio; (2021) Photonic VCSEL-neuron for spike-rate representation of digital image data. In: 2021 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC). 2021 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) . IEEE, DEU. ISBN 9781665418768 (https://doi.org/10.1109/cleo/europe-eqec52157.2021...)

[thumbnail of Hejda-etal-EQEC-2021-Photonic-VCSEL-neuron-for-spike-rate-representation-of-digital-image-data]
Preview
 Text. Filename: Hejda_etal_EQEC_2021_Photonic_VCSEL_neuron_for_spike_rate_representation_of_digital_image_data.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (877kB)| Preview

Abstract

With the increasing importance and capabilities of artificial intelligence (AI) approaches across all the domains of our society, the focus is now also moving towards the hardware used for running these algorithms. Neuromorphic computers, inspired by the computational capabilities of the brain, offer a viable, high efficiency alternative to usual von Neumann-based electronic processors. Besides the conventional CMOS-based electronics, novel technologies are also being investigated for neuromorphic computing [1] . Optics and photonics are one these promising alternative technologies for such systems, offering desirable properties including high bandwidth and low power operation. Among the photonic technologies for neuromorphic hardware, vertical cavity surface emitting lasers (VCSELs) provide mature fabrication technology, suitability for integration and high-speed operation.

CORE (COnnecting REpositories)