论文标题
知识蒸馏应用于光通道均衡：解决经常性连接的并行化问题
Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection
论文作者
论文摘要
为了避免复发性神经网络均衡器的非平行性，我们提出知识蒸馏以将RNN重塑为可行的前馈结构。后者显示出38 \％的潜伏期降低，同时仅影响0.5dB。
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.
