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To circumvent the non-parallelizability of recurrent neural network-basedequalizers, we propose knowledge distillation to recast the RNN into aparallelizable feedforward structure. The latter shows 38\% latency decrease,while impacting the Q-factor by only 0.5dB.

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