IronMan+alpha: Graph Neural Network and Reinforcement Learning in High-Level Synthesis

Related Papers

• [GLSVLSI'21] IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
• [TCAD'22] IronMan-Pro: Multi-objective Design Space Exploration in HLS via Reinforcement Learning and Graph Neural Network based Modeling

The proposed IronMan-Pro is a learning-based framework composed of CT (Code Transformer), GPP (GNN-based Performance Predictor), and RLMD (RL-based Multi-objective DSE). During training, IronMan-Pro takes HLS C/C++ code and IRs as inputs and the actual RTL performance (e.g., resource and timing) as the ground truth to train GPP and RLMD. During inference, the well-trained GPP provides graph embeddings and performance predictions to RLMD; the trained RLMD either finds optimized directives that satisfy user-specified design constraints such as available resources, or generates Pareto-solutions with various trade-offs between different resource types.

Prerequisites

• TensorFlow 3.6.x
• Stellargraph >= 1.2.0
• Vitis HLS
• Vivado

Dataset Generation

• Synthetic DFGs generation.
• After DFGs are generated, move them to DATASET.
• data_preprocess.py and generate_graph_datasets.py produce the necessary dataset files for GPP training.

GPP: GNN-based Performance Predictor

• Three GNN-based models to predict DSP, LUT, and critical path (CP) timing.
• After GPP is well trained, move the proxy models and the embedding models to RLMD.

RLMD: Rinforcement Learning based Multi-Objective Design Space Exploration

• Two RL methods are included in RLMD: actor critic, and policy gradient.
• hls_env.py: the environment.
• target_tuples.py: shuffle the tuples for RLMD training.

Meta-heuristics

• We include simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO).
• Note that these meta-heuristics also need the help of GPP.

Data Post-processing

• data_postprocess.ipynb: convert generated solutions to json files.
• Get_Perf.py: invoke Vitis HLS and Vivado to get the post-implementation resource usage and timing, given the json files (solutions) and cc files (designs in C++).

Contact

• If there is any question, please drop an email to nanwu@ucsb.edu
• If you find IronMan useful, please cite our paper:

  @inproceedings{wu2021ironman,
   title={Ironman: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning},
   author={Wu, Nan and Xie, Yuan and Hao, Cong},
   booktitle={Proceedings of the 2021 on Great Lakes Symposium on VLSI},
   pages={39--44},
   year={2021},
   organization={IEEE}
   }
  

  @ARTICLE{wu2022ironman,
   title={IronMan-Pro: Multi-objective Design Space Exploration in HLS via Reinforcement Learning and Graph Neural Network based Modeling},
   author={Wu, Nan and Xie, Yuan and Hao, Cong},
   journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, 
   year={2022},
   doi={10.1109/TCAD.2022.3185540}
   }