.png){kind=icon}
Return
Fast optimization of multichip modules using deep learning coupled with Bayesian method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.icheatmasstransfer.2022.106592.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this study, we develop an approach based on deep learning and the Bayesian method for fast optimization of the thermal placement of the multichip modules (MCMs). Specifically, a deep learning-based reduced-order model (ROM) is proposed to replace the CFD model to accelerate the optimization. The ROM applies the con-volutional neural network (CNN) as the coding layers, the deconvolutional CNN as the decoding layers, and the signed distance function (SDF) as the input of the ROM network. It enables fast and accurate prediction of the steady temperature field and the junction temperature of each chip with various chip numbers, chip powers, and chip placements. According to the results, the average error of the temperature field is only 0.81%, and the time cost for each temperature field prediction is less than 0.01 s, which is three orders faster than the numerical simulations. Profiting from the excellent performance of the ROM, the Bayesian optimization speeds up two orders of magnitude, and the coefficient of determination, R2, reaches 0.9991, which shows the huge potential of the deep learning-based optimization method to achieve the fast and accurate thermal placement design of the MCMs.
Keywords:
Multichip modules
Chip placement
Thermal optimization
Deep learning
Bayesian optimization
Journal
IF:
6.4
Papers:
1.0W
Citations:
2.5W
Organization
Cited Papers
ShareSaveUnsteady reduced-order model of flow over cylinders based on convolutional and deconvolutional neural network structure.png)
PHYSICS OF FLUIDS
PHYSICS OF FLUIDSIF4.3
PREAI
ShareSaveBayesian estimation of thermophysical parameters of thin metal films heated by fast laser pulses
PREAI
ShareSaveShareSave
View more