Guo Zhang*, Hao He*, Dina Katabi
(* indicates co-primary authors)
Check the ICML proceeding here. The oral presentation can be found here (starting from 1:09:40).
Use circuit.py to randomly generating parameterized square-resonator filters.
The following command, will generated and visualize a n-resonator circuits.
python circuit.py --num_resonator {n}You can download Circuit-GNN Dataset (27.6GB) via
wget http://circuit-gnn.csail.mit.edu/data.zipExtract the zip file and put them into your data_root. You should find a folder data and three data lists train/valid/test_list.txt under your data_root.
In the data folder there should be multiple subfolders with the name num{n}_type{t}. It contains all random generated circuits having n resonators and topology type t.
The total dataset have a size of 30GB.
The dataset is an updated version of the dataset we used for the original ICML paper.
- Larger Size: It has more samples of 4/5-resonator circuits for training and validation.
- More Balanced: It has a more balanced data amount for the circuits with different topology types.
- Harder Examples: The range a circuit parameters is adjusted a bit to include circuit with unusual s2p.
We provide an pre-trained model, Circuit-GNN Example. One could download it via
wget http://circuit-gnn.csail.mit.edu/exp_example.zipAfter extracting it, please move it the ./dump which is the default folder where all experiments folder will be put.
Then you can test, do forward prediction or inverse optimization with this pre-trained model via the following commands.
Start the training by the following command.
python train.py --exp {your_exp_name} --data_root {your_data_root}To customize training hyperparameters such as learning rate, batch size, please see the configuration file config.py.
The training result including model checkpoints and training logs will be stored in ./dump/{your_exp_name}.
python test.py --exp {your_exp_name} --data_root {your_data_root} --epoch {model_checkpoint_epoch}Following is an example result of Circuit-GNN.
+----------------+----------+-----------------+------------------+------------------+-----------------+
| # of resonator | topology | # of samples | train error (db) | valid error (db) | test error (db) |
+----------------+----------+-----------------+------------------+------------------+-----------------+
| 4 | 0 | 12000 1500 1500 | 0.670 | 2.085 | 2.062 |
| 4 | 1 | 12000 1500 1500 | 0.685 | 2.325 | 2.329 |
| 4 | 2 | 12000 1500 1500 | 0.579 | 1.422 | 1.468 |
| 4 | 3 | 12000 1500 1500 | 0.569 | 1.419 | 1.364 |
| 4 | 4 | 12000 1500 1500 | 0.534 | 1.294 | 1.279 |
| 4 | 5 | 12000 1500 1500 | 0.561 | 1.255 | 1.306 |
| 4 | 6 | 12000 1500 1500 | 0.683 | 2.037 | 2.029 |
| 4 | 7 | 12000 1500 1500 | 0.628 | 1.592 | 1.597 |
| 4 | 8 | 12000 1500 1500 | 0.670 | 1.706 | 1.694 |
| 4 | 9 | 12000 1500 1500 | 0.655 | 1.755 | 1.734 |
| 4 | avg | - | 0.623 | 1.689 | 1.686 |
| 5 | 0 | 20000 2500 2500 | 0.713 | 2.044 | 2.101 |
| 5 | 1 | 20000 2500 2500 | 0.705 | 1.871 | 1.864 |
| 5 | 2 | 20000 2500 2500 | 0.715 | 1.852 | 1.815 |
| 5 | 3 | 20000 2500 2500 | 0.710 | 1.857 | 1.823 |
| 5 | 4 | 20000 2500 2500 | 0.673 | 1.925 | 1.899 |
| 5 | 5 | 20000 2500 2500 | 0.732 | 2.173 | 2.137 |
| 5 | 6 | 20000 2500 2500 | 0.769 | 2.510 | 2.525 |
| 5 | 7 | 20000 2500 2500 | 0.766 | 2.499 | 2.494 |
| 5 | 8 | 20000 2500 2500 | 0.778 | 2.815 | 2.829 |
| 5 | avg | - | 0.729 | 2.172 | 2.165 |
| 3 | 0 | 0 0 1000 | - | - | 1.490 |
| 3 | 1 | 0 0 1000 | - | - | 1.362 |
| 3 | 2 | 0 0 1000 | - | - | 1.260 |
| 3 | 3 | 0 0 1000 | - | - | 3.298 |
| 3 | avg | - | - | - | 1.853 |
| 6 | 0 | 0 0 900 | - | - | 4.073 |
| 6 | 1 | 0 0 900 | - | - | 3.792 |
| 6 | 2 | 0 0 900 | - | - | 4.130 |
| 6 | 3 | 0 0 900 | - | - | 4.103 |
| 6 | 4 | 0 0 900 | - | - | 2.646 |
| 6 | 5 | 0 0 900 | - | - | 3.391 |
| 6 | avg | - | - | - | 3.689 |
+----------------+----------+-----------------+------------------+------------------+-----------------+Note that the error is a bit worse than the result in the original paper. It is due to that there are more hard examples (circuits with weired s2p functions) in this updated dataset. In the original dataset, we included more easier data sample. For example, 4-resonator circuits with topology type 2. Based on human knowledge, this topology is known to deliver more regular s2p functions like filters. We were biasing our model at that time since we were focus on using the model to design/optimize filters.
To visualize the forward prediction of Circuit GNN on n-resonator circuits with topology type tp, one can just run the following command.
Choose phase from train | valid | test to visualize result at train/validation/test dataset.
python vis_forward.py --data_root {your_data_root} --exp {your_exp_name} --epoch {checkpoint epoch} --num_resonator {n} --circuit_type {tp} --phase {phase} Simply run the following command, you will see an example of how the model generating a filter having a passband from 260 GHz to 290 GHz.
python inverse.py --exp {your_exp_name} --epoch {checkpoint epoch} Finding our dataset and code base useful? Please consider citing:
@inproceedings{he2019circuit,
title={Circuit-GNN: Graph neural networks for distributed circuit design},
author={Zhang, Guo and He, Hao and Katabi, Dina},
booktitle={International Conference on Machine Learning},
pages={7364--7373},
year={2019}
}
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