@inproceedings{249c9a9c45f8475ba2a8e7232a289e47,
title = "Machine Learning for 3D-IC Electric-Thermal Simulation and Management",
abstract = "Thermal management for 3-D ICs is not only important but also challenging. While air-cooled heat sink is agreed to become incapable for 3-D ICs, microchannel cooling has provided a better solution. In this paper, a machine learning method, Bayesian Optimization (BO), is applied in 3-D ICs with a time-dependent power map to intelligently control the flow rates of the tier-specific microfluidic heatsink (MFHS) for dynamic thermal management (DTM).",
author = "Li, {Yong Sheng} and Li, {Er Ping} and Huan Yu and Hanju Oh and Bakir, {M. S.} and M. Swaminathan",
note = "Funding Information: This work was supported by the National Science Foundations of China under the Grant 61571395. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Computational Electromagnetics, ICCEM 2018 ; Conference date: 26-03-2018 Through 28-03-2018",
year = "2018",
month = oct,
day = "17",
doi = "10.1109/COMPEM.2018.8496543",
language = "English (US)",
series = "2018 IEEE International Conference on Computational Electromagnetics, ICCEM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on Computational Electromagnetics, ICCEM 2018",
address = "United States",
}