Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem...
Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
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Full title
Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
Author / Creator
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
arXiv.org, 2022-12
Language
English
Formats
Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure. The latter shows 38\% latency decrease, while impacting the Q-factor by only 0.5dB.
Alternative Titles
Full title
Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
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Record Identifier
TN_cdi_proquest_journals_2753449155
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2753449155
Other Identifiers
E-ISSN
2331-8422
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