Transient Simulations of High-Speed Channels Using CNN-LSTM With an Adaptive Successive Halving Algorithm for Automated Hyperparameter Optimizations

Transient Simulations of High-Speed Channels Using CNN-LSTM With an Adaptive Successive Halving Algorithm for Automated Hyperparameter Optimizations

Transient simulations of high-speed channels can be very time intensive. Recurrent neural network (RNN) based methods can be used to speed up the process by training a RNN model on a relatively short bit sequence, and then using a multi-steps rolling forecast method to predict subsequent bits. Howev...

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Bibliographic Details
Main Authors: Chan Hong Goay, Nur Syazreen Ahmad, Patrick Goh
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Automated hyperparameter optimization
convolutional neural network (CNN)
high-speed channel
long short-term memory (LSTM) network
progressive sampling
transient simulation
Online Access:https://ieeexplore.ieee.org/document/9536702/

Internet

https://ieeexplore.ieee.org/document/9536702/

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