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arXiv · 2212.04569

Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

Sasipim Srivallapanondh·Pedro J. Freire·Bernhard Spinnler·Nelson Costa·Antonio Napoli·Sergei K. Turitsyn·Jaroslaw E. Prilepsky
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Abstract

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.

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Sasipim Srivallapanondh, Pedro J. Freire, Bernhard Spinnler, Nelson Costa, Antonio Napoli, Sergei K. Turitsyn, Jaroslaw E. Prilepsky. 2022-12-08. Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection. https://arxiv.org/abs/2212.04569

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