Encoder decoder based generative networks for static and transient thermal analysis. This repository contains code for the paper titled "Thermal and IR Drop Analysis Using ConvolutionalEncoder-Decoder Networks".
Fig.7 of the paper is a video which is available below. Please click here and download and view in any image viewer.
*Video comparing the prediction of transient ThermEDGe against commercial tool-generated temperature contours for Testcase 6: (i) left video shows the time varying power map, (ii) center video shows the commercially-generated temperature contours, and (iii) right video shows ThermEDGe-generated temperature contours |
git clone https://github.com/VidyaChhabria/EDGe-Thermal-Analysis.git
Bare minimum dependencies are the following:
Create virtual environment and install the required python packages
python3 -m venv EDGe
source EDGe/bin/activate
pip3 install -r requirements.txt
python3 src/transient_thermal_model.py -train_data_path ./data/data_set_2/train/Transient_runs -test_data_path ./data/data_set_2/test/Transient_runs -output_plot ./output/.
Argument | Comments |
---|---|
-h, --help | Prints out the usage |
-train_data_path |
Path to the training data runs (required, str) |
-test_data_path |
Path to the testing data (str, required) |
-output_plot |
Path to generate the output plots (required,str) |
Note:
V. A. Chhabria, V. Ahuja, A. Prabhu, N. Patil, P. Jain, and S. S. Sapatnekar, “Thermal and IR Drop Analysis Us-ing Convolutional Encoder-Decoder Networks,” Proc. of Asia and South Pacific Design Automation Conference, 2021.