{"title":"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","authors":"Yixuan Zhao, Thong Nguyen, J. Schutt-Ainé","doi":"10.1109/SPI54345.2022.9874932","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":285253,"journal":{"name":"2022 IEEE 26th Workshop on Signal and Power Integrity (SPI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Application of Neural Network Based Cascade-able Transceiver Model in Serial Link Simulation\",\"authors\":\"Yixuan Zhao, Thong Nguyen, J. Schutt-Ainé\",\"doi\":\"10.1109/SPI54345.2022.9874932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":285253,\"journal\":{\"name\":\"2022 IEEE 26th Workshop on Signal and Power Integrity (SPI)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 26th Workshop on Signal and Power Integrity (SPI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPI54345.2022.9874932\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 26th Workshop on Signal and Power Integrity (SPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPI54345.2022.9874932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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