基于神经网络的可级联收发器模型在串行链路仿真中的应用

Yixuan Zhao, Thong Nguyen, J. Schutt-Ainé
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引用次数: 2

摘要

本文介绍了利用前馈神经网络(FNN)建立信道级联黑匣子收发器模型的工作流程。与晶体管级模型相比,FNN模型不依赖于电路模拟器,而前者的牛顿-拉夫森迭代速度较慢。此外,FNN模型的生成不需要大量的电路设计知识,使其对从事其他领域工作的最终用户更可行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Neural Network Based Cascade-able Transceiver Model in Serial Link Simulation
This paper describes the work-flow of developing black-box transceiver models for channel cascading using feedforward neural network (FNN). Compared to transistor level models, the FNN model is independent of the circuit simulator, whereas the former suffers from slow Newton-Raphson iterations. Furthermore, the generation of FNN model requires no substantial knowledge in circuit design, making it more feasible to the end-users who work in another field.
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