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arXiv 2208.12866eess.SPcs.CCcs.ETcs.LG
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降低光信道均衡中神经网络的计算复杂度:从概念到实现

Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation

Aston Institute of Photonic Technologies, Aston University(阿斯顿大学阿斯顿光子技术研究所);Infinera R&D(英飞朗研发部);Infinera Unipessoal, Lda(英飞朗Unipessoal有限公司)

Pedro J. Freire, Antonio Napoli, Diego Arguello Ron, Bernhard Spinnler, Michael Anderson, Wolfgang Schairer, Thomas Bex, Nelson Costa, Sergei K. Turitsyn, Jaroslaw E. Prilepsky

AI总结:

本文提出一种低复杂度开发神经网络均衡器的方法,通过贝叶斯优化辅助的量化、权重聚类和剪枝等压缩策略,在降低复杂度的同时提升性能,优于传统数字背向传播均衡器。

摘要:

本文提出了一种新方法,能够低复杂度地开发基于神经网络(NN)的均衡器,以减轻高速相干光传输系统中的损伤。本文全面描述并比较了应用于前馈和循环NN设计的各种深度模型压缩方法,并评估了这些策略对每种NN均衡器性能的影响。研究考虑了量化、权重聚类、剪枝及其他前沿模型压缩策略。本文提出并评估了一种贝叶斯优化辅助的压缩方法,其中压缩的超参数被选择为在降低复杂度的同时提升性能。最终,通过利用仿真和实验数据评估了每种压缩方法的复杂度与其性能之间的权衡,以完成分析。通过使用最优压缩方法,我们表明,设计一个实现更简单且性能优于每跨度单步的传统数字背向传播(DBP)均衡器的基于NN的均衡器是可能的。这是通过在应用加权聚类和剪枝算法后减少NN均衡器中使用的乘法器数量来实现的。此外,我们证明基于NN的均衡器也能在保持与全电子色散补偿块相同复杂度的同时实现更优性能。我们通过强调开放问题和现有挑战以及可能的未来研究方向来总结我们的分析。

英文摘要:

In this paper, a new methodology is proposed that allows for the low-complexity development of neural network (NN) based equalizers for the mitigation of impairments in high-speed coherent optical transmission systems. In this work, we provide a comprehensive description and comparison of various deep model compression approaches that have been applied to feed-forward and recurrent NN designs. Additionally, we evaluate the influence these strategies have on the performance of each NN equalizer. Quantization, weight clustering, pruning, and other cutting-edge strategies for model compression are taken into consideration. In this work, we propose and evaluate a Bayesian optimization-assisted compression, in which the hyperparameters of the compression are chosen to simultaneously reduce complexity and improve performance. In conclusion, the trade-off between the complexity of each compression approach and its performance is evaluated by utilizing both simulated and experimental data in order to complete the analysis. By utilizing optimal compression approaches, we show that it is possible to design an NN-based equalizer that is simpler to implement and has better performance than the conventional digital back-propagation (DBP) equalizer with only one step per span. This is accomplished by reducing the number of multipliers used in the NN equalizer after applying the weighted clustering and pruning algorithms. Furthermore, we demonstrate that an equalizer based on NN can also achieve superior performance while still maintaining the same degree of complexity as the full electronic chromatic dispersion compensation block. We conclude our analysis by highlighting open questions and existing challenges, as well as possible future research directions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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