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https://github.com/Yu-Maryland/Gamora
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks (DAC'23)
https://github.com/Yu-Maryland/Gamora
Last synced: about 2 months ago
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Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks (DAC'23)
- Host: GitHub
- URL: https://github.com/Yu-Maryland/Gamora
- Owner: Yu-Maryland
- License: other
- Created: 2023-04-10T21:01:07.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-11-07T19:05:38.000Z (2 months ago)
- Last Synced: 2024-11-09T04:03:51.502Z (2 months ago)
- Language: C
- Size: 22.3 MB
- Stars: 47
- Watchers: 3
- Forks: 5
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-opensource-hardware - gamora
README
# Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks (DAC'23)
## Paper
https://arxiv.org/pdf/2303.08256.pdf
```bash
@article{wu2023gamora,
title={Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks},
author={Wu, Nan and Li, Yingjie and Hao, Cong and Dai, Steve and Yu, Cunxi and Xie, Yuan},
journal={Design Automation Conference (DAC'23)},
year={2023}
}
```
## A more generic framework for Boolean/RTL Graph Learning -- [V2PYG](https://yu-maryland.github.io/Verilog-to-PyG/)
We are developing a more generic V2PYG framework to provide versatile capabilities for converting RTL designs into foundational graph representations, while also facilitating RTL-equivalent argumentation. [https://ycunxi.github.io/Verilog-to-PyG](https://yu-maryland.github.io/Verilog-to-PyG/)
## Installation
### Prereq:
1) Python packages: torch and torch_geometric
2) Package: readline (follow ABC installation requirement)
sudo apt-get install libreadline6 libreadline6-dev (Ubuntu)
sudo yum install readline-devel (CentOS, RedHat)
### Installation
compile ABC customized for graph learning
cd abc;make clean;make -j4
## Pre-processed Dataset - CSA (techmapped) and Booth Multipliers
Check at Huggingface: https://huggingface.co/datasets/yucx0626/Gamora-CSA-Multiplier/tree/main
## Implementation
#### 1) Data generator
```python
dataset_prep/dataset_generator.py
class ABCGenDataset
```
- gentype =0 CSA-array Multiplier generation and labeling
- gentype =1 CPA Adder generation and labeling
- gentype =2 Read a design and generate dataset
- gentype =3 Generate Booth-encoded multiplier (tbd)
**Note: ABC is required (../abc) (make sure to create a link symbol of abc binary
in this folder)**
```bash
ln -s ../abc/abc .
```
***ABC implementation for data generation.***
```c
// abc/src/proof/acec/acecXor.c
class Gia_EdgelistMultiLabel()
```
#### 2) Train-Test Demo - training on 8-bit CSA and predicting on 32-bit CSA
* Dataset generation
```python
python ABC_dataset_generation.py --bits 8
# generate an 8-bit CSA multiplier
```
```python
python ABC_dataset_generation.py --bits 32
# generate a 32-bit CSA multiplier
```
* Training and inference
```python
python gnn_multitask.py --bits 8 --bits_test 32
# training with mult8, and testing with mult32
```
* Inference with pre-trained model
```python
python gnn_multitask_inference.py --model_path SAGE_mult8 --bits_test 32 --design_copies 1
# load the pre-trained model "SAGE_mult8", and test with mult32
```
*Training INPUT*: 8-bit CSA-Mult
*Testing INPUT*: 32-bit CSA-Mult
```bash
# training
Highest Train: 99.45
Highest Valid: 100.00
Final Train: 98.90
Final Test: 99.12
# testing
mult32
Highest Train: 0.00 ± nan
Highest Valid: 0.00 ± nan
Final Train: 0.00 ± nan
Final Test: 99.95 ± nan
```
#### New commands for ABC
```bash
abc 01> edgelist -h
usage: edgelist : Generate pre-dataset for graph learning (MPNN,GraphSAGE, dense graph matrix)
-F : Edgelist file name (*.el)
-c : Class map for corresponding edgelist (Only for GraphSAGE; must has -F -c -f all enabled)
-f : Features of nodes (Only for GraphSAGE; must has -F -c -f all enabled)
-L : Switch to logic netlist without labels (such as AIG and LUT-netlist)
Example 1 (GraphSAGE dataset)
read your.aig; edgelist -F test.el -c test-class-map.json -f test-feats.csv
Example 2 (Generate dataset for LUT-mapping netlist; unsupervised)
read your.blif; strash; if -K 6; edgelist -L -F lut-test.el
Example 3 (Generate dataset for abstraction; supervised for FA/HA extraction - GraphSAGE)
read your.blif; strash; &get; &edgelist -F test.el -c test-class_map.json -f test-feats.csv
```