Circuit data for GANA

GANA: Graph Convolutional Network Based Automated Netlist Annotation for Analog Circuits

The code in this repository reads multiple OTA circuits graphs and processs them to prepare inputs for GNN.

The inputs needed for GNN are:

1. N by N adjacency matrix (N is the number of nodes),
2. N by D feature matrix (D is the number of features per node), and
3. N by E binary label matrix (E is the number of classes).

Creating the input data for GANA:

To create graph from spice files:

python3 src/read_netlist.py -d circuit_data/OTA_data/spice.zip

To extract features from the graphs a which is used by the classifier

python3 src/preprocess_data.py -d circuit_data/OTA_data/

This creates a processed file in the circuit_data/OTA_data directory which will be used for subcircuit identification

Cite

Please cite our paper if you use our benchmarks in your own work:

@INPROCEEDINGS{GANA2020,
  author={Kunal, Kishor and Dhar, Tonmoy and Madhusudan, Meghna and Poojary, Jitesh and Sharma, Arvind and Xu, Wenbin and Burns, Steven M. and Hu, Jiang and Harjani, Ramesh and Sapatnekar, Sachin S.},
  booktitle={2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, 
  title={GANA: Graph Convolutional Network Based Automated Netlist Annotation for Analog Circuits}, 
  year={2020},
  pages={55-60}
  }