AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks

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Tim Bücher, Lilas Alrahis, Guilherme Paim, Sergio Bampi, Ozgur Sinanoglu, Hussam Amrouch

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AppGNN performs node-level classification for functional reverse engineering of flattened gate-level netlists. AppGNN builds on top of GNN-RE in order to reverse engineer approximate circuits.

In the case of using our framework, please use the following citation

@inproceedings{appgnn,
  author = {Buecher, Tim and Alrahis, Lilas and Bampi, Sergio and Paim, Guilherme and Sinanoglu, Ozgur and Amrouch, Hussam},
  booktitle = {IEEE/ACM 41st International Conference on Computer-Aided Design (ICCAD'22)},
  month = {11},
  title = {AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks},
  year = 2022
}

Graph sampling

The main contributions of this work lies within the novel graph sampling approach. It generates an approximate variant of an exact implementation in order to mimic a generic approximation technique. This functionality is implemented in the following files:

graph_leaf_sampling.py

and

graph_random_sampling.py

More information on these approaches can be found in our Paper in section 3.4.

Requirements

The following dependencies are required to be installed:

$ conda create --name myenv python=3.6.8 tensorflow=1.12.0
$ conda activate myenv
$ conda install -c anaconda numpy=1.14.3
$ conda install -c anaconda scipy=1.1.0 
$ conda install -c anaconda scikit-learn=0.19.1
$ conda install -c anaconda pyyaml=3.12
$ conda install -c conda-forge openmp=4.0
$ conda install -c anaconda cython=0.29.2

GraphSaint installation (in the root directory, next to ./Netlist_to_graph):

$ git clone https://github.com/GraphSAINT/GraphSAINT.git
$ cd GraphSAINT
$ python graphsaint/setup.py build_ext --inplace

Usage

The following scripts are required to convert gate-level netlists to graphs that can be used in AppGNN:

./Netlist_to_graph/Parsers/netlist_to_graph.pl
./Netlist_to_graph/Parsers/theCircuit.pm
./Netlist_to_graph/Parsers/graph_parser.py
./Netlist_to_graph/Parsers/graph_leaf_sampling.py
./Netlist_to_graph/Parsers/graph_random_sampling.py

In ./Netlist_to_graph/Circuits_datasets/example you can find an example training dataset that contains various circuits used for training and validation (originally obtained from GNN-RE. In the same directory, you will also find the circuits of exact adder implementations (*_Xbit_approxlevel_Y.v) that will be used by AppGNN to generate a generic approximated variant of adder circuits. Finally, a true approximate circuit, obtained from the EvoApproxLib (Test_*.v), is located in the same directory. This circuit is used to test the accuracy of AppGNN and can be replaced by any approximate adder circuit. Replace graph_leaf_sampling.py with graph_random_sampling.py to perform random node sampling instead of leaf node sampling in the following.

To run the example, execute the following:

$ cd ./Netlist_to_graph/Graphs_datasets/example
$ perl ../../Parsers/netlist_to_graph.pl -i ../../Circuits_datasets/example
$ cp ../../Parsers/graph_parser.py .
$ cp ../../Parsers/graph_leaf_sampling.py .
$ python graph_leaf_sampling.py
$ python graph_parser.py
$ cd ../../../GraphSAINT
$ python -m graphsaint.tensorflow_version.train --data_prefix ../Netlist_to_graph/Graphs_datasets/example --train_config ../config.yml --gpu -1

Acknowledgement

We would like thank Hanqing Zeng for making GraphSAINT code available.