Fast Response Prediction Method Based on Bidirectional Long Short-Term Memory for High-Speed Links

被引:8
|
作者
Luo, Yuhuan [1 ]
Chu, Xiuqin [1 ]
Yuan, Haiyue [1 ]
Wei, Tao [1 ]
Wang, Jun [1 ]
Wu, Feng [1 ]
Li, Yushan [1 ]
机构
[1] Xidian Univ, Minist Educ, Key Lab High Speed Circuit Design & EMC, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Bidirectional long short-term memory (Bi-LSTM); high-speed link; nonlinearity; recurrent neural network (RNN); signal integrity (SI); EYE DIAGRAM; DESIGN;
D O I
10.1109/TMTT.2022.3233303
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
There is a recognized need to evaluate the performance of high-speed links accurately and quickly with increasing clock frequency. To evaluate the performance of the systems, the transient simulation method and the fast time-domain simulation method are adopted. Unfortunately, neither method can guarantee both the efficiency and accuracy of the results at the same time. In this article, a modeling method that can overcome the shortcomings of both the transient simulation method and the fast time-domain simulation method is proposed for obtaining responses of high-speed links based on bidirectional long short-term memory (Bi-LSTM). Here, the pulse response and response of a certain number of bits are first simulated or measured, which are used to determine training datasets. Then, the Bi-LSTM-based model uses the generated training datasets to build a precise model. Next, the developed model predicts the response of the high-speed link. Compared to the transient simulation method and fast time-domain simulation method, the results show that the proposed method can consider both accuracy and efficiency. In addition, 33 simulation experiments are performed to demonstrate the robustness of the proposed method. Finally, the accuracy of the proposed method is validated by comparing the measured and predicted results of the high-speed links with different nonlinearities.
引用
收藏
页码:2347 / 2359
页数:13
相关论文
共 50 条
  • [31] Short-Term Prediction of Wind Power Based on Deep Long Short-Term Memory
    Qu Xiaoyun
    Kang Xiaoning
    Zhang Chao
    Jiang Shuai
    Ma Xiuda
    2016 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2016, : 1148 - 1152
  • [32] Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory
    XUE Wendong
    CHAI Yuan
    LI Qigan
    HONG Yongqiang
    ZHENG Gaofeng
    Instrumentation, 2018, 5 (04) : 46 - 54
  • [33] Research on short-term disease risk prediction based on long short-term memory
    Feng, Yanjun
    Wang, Hongxia
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2021, 128 : 176 - 176
  • [34] Short-term wind power prediction based on combined long short-term memory
    Zhao, Yuyang
    Li, Lincong
    Guo, Yingjun
    Shi, Boming
    Sun, Hexu
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2024, 18 (05) : 931 - 940
  • [35] Time Series-based Spoof Speech Detection Using Long Short-term Memory and Bidirectional Long Short-term Memory
    Mirza, Arsalan R.
    Al-Talabani, Abdulbasit K.
    ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 2024, 12 (02): : 119 - 129
  • [36] Remaining useful life prediction method for bearing based on parallel bidirectional temporal convolutional network and bidirectional long and short-term memory network
    Liang H.-P.
    Cao J.
    Zhao X.-Q.
    Kongzhi yu Juece/Control and Decision, 2024, 39 (04): : 1288 - 1296
  • [37] A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network
    Hu, Wenlong
    Ji, Bowen
    Gao, Kunpeng
    SENSORS, 2024, 24 (16)
  • [38] HIGH-SPEED SELF-TERMINATING SEARCH OF SHORT-TERM MEMORY
    ANDERS, TR
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 1973, 97 (01): : 34 - 40
  • [39] Short-Term Memory Sampling for Spread Measurement in High-Speed Networks
    Du, Yang
    Huang, He
    Sun, Yu-E
    Chen, Shigang
    Gao, Guoju
    Wang, Xiaoyu
    Xu, Shenghui
    IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2022), 2022, : 470 - 479
  • [40] Short-term Vehicle Speed Prediction Based on Convolutional Bidirectional LSTM Networks
    Han, Shaojian
    Zhang, Fengqi
    Xi, Junqiang
    Ren, Yanfei
    Xu, Shaohang
    2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC), 2019, : 4055 - 4060