GridNet

GridNet is a GAN-based IR Drop Analysis Tool for On-Chip Power Grid Networks

• Input: image-like multi-channel tensor, e.g. 4-channel input consisting column resistance, row resistance, current source and time.
• Output: single-channel IRDrop map with the same size of input.
• Can also provide sensitivity(gradient) info for specified nodes. Please refer to the Examples section.

32x32 GridNet Train Process

Installation

GridNet requires TensorFlow 1.x to be installed as backend. It was tested on Fermi server in Anaconda virtual env with following dependencies:

• python=3.7.3
• tensorflow-gpu=1.14.0
• cudatoolkit=10.1.168
• numpy
• matplotlib

Examples

• Train 32x32 GridNet:

$ python gridnet.py --input-imgsize 32 --input-channel 4 --data-dir /fermi_data/shared/IRGAN/data32/mortal3000/,/fermi_data/shared/IRGAN/data32/mortal6000/,/fermi_data/shared/IRGAN/data32/mortal9000/,/fermi_data/shared/IRGAN/data32/mortal12000/,/fermi_data/shared/IRGAN/data32/immortal3000/,/fermi_data/shared/IRGAN/data32/immortal6000/,/fermi_data/shared/IRGAN/data32/immortal9000/,/fermi_data/shared/IRGAN/data32/immortal12000/ --epochs 100 --batch-size 8 --lr 0.000001

• Inference 32x32 GridNet: Paths of all test data must be provided through --test-csv.

$ python gridnet.py --input-imgsize 32 --input-channel 4 --is-inference --ckpt-file /fermi_data/wjin/GridNet/Samples/GridNet_Train_Size_32_20211013164554/checkpoints/GridNet.ckpt-99 --test-csv /fermi_data/wjin/GridNet/Samples/GridNet_Train_Size_32_20211013164554/test_data.csv

• Inference 32x32 GridNet with both sensitivities and raw outputs saved into .csv files: Set --is-gradient will enable the sensitivity(gradient) calculation. A .csv file which specifies which nodal voltages' sensitivity are to be calculated must be provided through --grad-node-list.

$ python gridnet.py --input-imgsize 32 --input-channel 4 --is-inference --ckpt-file /fermi_data/wjin/GridNet/Samples/GridNet_Train_Size_32_20211013164554/checkpoints/GridNet.ckpt-99 --test-csv /fermi_data/wjin/GridNet/Samples/GridNet_Train_Size_32_20211013164554/test_data.csv --save-output-csv --is-gradient --grad-node-list ./sensitivity.csv

Note

To run the old version used in original Gridnet ICCAD'20 paper, please checkout the old eb1fc20 version using:

git checkout eb1fc208a6f7f3449fd86271997d03bf2b94609c

The old version could support only one current source while the later versions (starting from c9c78da9c93e9f0a0e641d5db0b515300f87e392) support a curernt source grid.

Publications

@inproceedings{ZhouJin:ICCAD'20,
  author = {Zhou, H. and Jin, W. and Tan, S. X.-D.},
  title = {{GridNet: Fast Data-Driven EM-Induced IR Drop Prediction and Localized Fixing for On-Chip Power Grid Networks}},
  booktitle = {{Proceedings of the 39th International Conference on Computer-Aided Design}},
  series = {ICCAD '20},
  year = {2020},
  month = nov,
  pages = {1--9},
  location = {Virtual Event},
}

The Team

GridNet was originally developed by Wentian Jin and Han Zhou at VSCLAB under the supervision of Prof. Sheldon Tan.

GridNet is currently maintained by Wentian Jin.