README.md

PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs

We propose PowerGear, a graph-learning-assisted power estimation approach for FPGA HLS, which features high accuracy, efficiency and transferability. PowerGear comprises two main components:

• a graph construction flow
• a customized graph neural network (GNN) model

Compared with on-board measurement, PowerGear estimates total and dynamic power for new HLS designs with errors of 3.60% and 8.81%, respectively, which outperforms the prior arts in research and the commercial product Vivado. In addition, PowerGear demonstrates a speedup of 4× over Vivado power estimator. Finally, we present a case study in which PowerGear is exploited to facilitate design space exploration for FPGA HLS, leading to a performance gain of up to 11.2%, compared with methods using state-of-the-art predictive models.

More details could be found in our DATE 2022 paper. If PowerGear is helpful to your works, please cite:

@inproceedings{lin2022powergear,
  title={PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs},
  author={Lin, Zhe and Yuan, Zike and Zhao, Jieru and Zhang, Wei and Wang, Hui and Tian, Yonghong},
  booktitle={Procs. of Design, Automation and Test in Europe Conference and Exhibition (DATE)},
  year={2022}
}

PowerGear Overview

In the training stage, graph- structured samples are constructed using HLS results, ground truth power values are collected from real measurement on board after RTL implementation, and a transferable GNN model is trained; in the inference stage, graph samples for new designs are generated after HLS runs and the trained GNN model is seamlessly employed for power prediction.

How to Construct Graphs from HLS

Please look at the Readme.md file of each subfolder inside graph_construction folder. All the program infromation can be found.

How to Train, Test and Evaluate Our Models

Please look at the Readme.md file of each subfolder inside HEC_GNN folder. All the program infromation and pretrained models can be found.

File Architecture

.
├── dataset                         # Paper dataset
│   ├── benchmark                   # Benchmark application
│   └── graph_sample                # Graph sample dataset used in HEC_GNN                             
├── hec_gnn                         # The HEC_GNN model
│   ├── cov                         # Graph convolution layer
│   ├── ensemble_model              # Ensemble model
│   ├── single_model                # Single model
│   ├── HEC_GNN_env_install.sh      # The environment installation script
│   └── README.md                   
├── graph_construction              # The graph construction
│   ├── README.md                 
│   ├── feature_extraction          # Feature extraction flow
│   └── sample_generation           # Sample generation flow
├── paper_model                     # Trained model in our paper
│   ├── test_result                 # The test results for each dataset in our paper.
│   └── trained_model               # The trained model for each dataset in our paper.
├── pic                             
├── utils                           # Some basic functions
└── DATE_22_PowerGear.pdf           # Paper

License

MIT License

Contact

For more information please contact Zhe Lin, Zike Yuan.

Zhe Lin : [email protected]

Zike Yuan: [email protected]