Comprehensive performance analysis of a VCSEL-based photonic reservoir computer
| Autor: | Julian Bueno, Matej Hejda, Joshua Robertson, Antonio Hurtado |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science business.industry TK Reservoir computing Physics::Optics Optical computing Optical polarization Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Vertical-cavity surface-emitting laser Semiconductor laser theory QC350 Electronic engineering Electrical and Electronic Engineering Photonics Reduced cost business |
| ISSN: | 1041-1135 |
| Popis: | Optical neural networks offer radically new avenues for ultrafast, energy-efficient hardware for machine learning and artificial intelligence. Reservoir Computing (RC), given its high performance and cheap training has attracted considerable attention for photonic neural network implementations, principally based on semiconductor lasers (SLs). Among SLs, Vertical Cavity Surface Emitting Lasers (VCSELs) possess unique attributes, e.g. high speed, low power, rich dynamics, reduced cost, ease to integrate in array architectures, making them valuable candidates for future photonic neural networks. This work provides a comprehensive analysis of a telecom-wavelength GHz-rate VCSEL RC system, revealing the impact of key system parameters on its performance across different processing tasks. |
| Databáze: | OpenAIRE |
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