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A robust analog circuit parameter optimization framework with sampling-efficient reinforcement learning

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RoSE

A robust analog circuit parameter optimization framework with high sampling efficience by synergistically combining Bayesian optimization (BO) and reinforcement learning (RL).

About this work

For more details, please refer to our DAC'23 paper: RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning

And our TCAD'24 paper: RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning

How to Use

Details and code regarding Cadence simulation will be updated soon.

Environment Setup

This setup requires Anaconda. Run the following command below:

conda env create -f environment.yml

To activate the environment:

conda activate RoSE

Generate Specs

python gen_specs.py --num_specs ##

RoSE

BO for optimizing the starting point

# cd to your Cadence folder
cd tso
python tso_BO.py

After getting a suboptimal parameter from BO, convert it to a discrete index in the design space

# post proccess all suboptimal parameters from BO to index 
# replace this index in line 169 in gym_tso/envs/RL_env.py for training
self.cur_params_idx = np.array([33, 20, 4, 16, 13, 12, 13, 25, 12, 40])
# replace this index in line 169 in gym_tso_DE/envs/RL_env.py for deployment
self.cur_params_idx = np.array([33, 20, 4, 16, 13, 12, 13, 25, 12, 40])

RL training

# cd to your Cadence folder
python main_train_BORL.py

RL deployment

# cd to your Cadence folder
python DE_tso_BORL.py

Citation

If you use this framework for your research, please cite our DAC'23 paper:

@inproceedings{gao2023rose,
  title={RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning},
  author={Gao, Jian and Cao, Weidong and Zhang, Xuan},
  booktitle={2023 60th ACM/IEEE Design Automation Conference (DAC)},
  pages={1--6},
  year={2023},
  organization={IEEE}
}

our TCAD'24 paper:

@article{cao2024rose,
  title={Rose-opt: Robust and efficient analog circuit parameter optimization with knowledge-infused reinforcement learning},
  author={Cao, Weidong and Gao, Jian and Ma, Tianrui and Ma, Rui and Benosman, Mouhacine and Zhang, Xuan},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2024},
  publisher={IEEE}
}

Contact Information

If you have any questions regarding using this framework, please feel free to contact us at [email protected].

Version History

  • 0.1
    • Initial Release

License

This framework is licensed under the GNU3 License - see the LICENSE.md file for details

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