SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Szczerba Krzysztof 1985) srt2:(2020-2023)"

Sökning: WFRF:(Szczerba Krzysztof 1985) > (2020-2023)

  • Resultat 1-2 av 2
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • Srinivasan, Muralikrishnan, 1991, et al. (författare)
  • End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
  • 2023
  • Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 41:11, s. 3261-3277
  • Tidskriftsartikel (refereegranskat)abstract
    • Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models.
  •  
2.
  • Srinivasan, Muralikrishnan, 1991, et al. (författare)
  • Learning Optimal PAM Levels for VCSEL-based Optical Interconnects
  • 2022
  • Ingår i: 2022 European Conference on Optical Communication, ECOC 2022. - 9781957171159
  • Konferensbidrag (refereegranskat)abstract
    • An auto-encoder that optimizes a VCSEL-based fiber-optic system end-to-end and provides a 1.5dB sensitivity gain at higher temperatures is trained, utilizing a neural network that models the response of a VCSEL for a range of operating temperatures.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-2 av 2

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy