DLAG-TA: Deep Learning-Based Adaptive Grid Builder for System-Level Thermal Analysis

Wen Sheng Lo, Hong Wen Chiou, Shih Chieh Hsu, Yu Min Lee

研究成果: Conference contribution同行評審

摘要

This work develops an adaptive grid builder for system-level thermal simulators by embedding a well trained deep neural network (DNN) model, and we call it DLAG-TA. To automatically generate adaptive grids for various sizes/structures of handheld devices, it also contains a parameter scaling procedure and a virtual block to element grid mapping procedure to assist the trained DNN model. The results show that DLAG-TA can effectively build grids with high quality.

原文English
主出版物標題Proceedings of the 20th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2021
發行者IEEE Computer Society
頁面477-483
頁數7
ISBN(電子)9781728185392
DOIs
出版狀態Published - 2021
事件20th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2021 - Virtual, San Diego, 美國
持續時間: 1 6月 20214 6月 2021

出版系列

名字InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITHERM
2021-June
ISSN(列印)1936-3958

Conference

Conference20th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2021
國家/地區美國
城市Virtual, San Diego
期間1/06/214/06/21

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