LLM-Enhanced Bayesian Optimization for Efficient Analog Constraint Generation

Code for arxiv paper: https://arxiv.org/abs/2406.05250

LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation

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1. Setup

1. If using OpenAI, set up environment variables:

echo "export OPENAI_API_KEY={api_key}" >> ~/.zshrc
echo "export OPENAI_API_VERSION={api_version}" >> ~/.zshrc
## Note: these might be optional
echo "export OPENAI_API_BASE={api_base}" >> ~/.zshrc
echo "export OPENAI_API_TYPE={api_type}" >> ~/.zshrc

In our experiments, we used gpt-turbo-3.5 for all modules and gpt-turbo-3.5-instruct for the generative surrogate model (Note: these models might require separate set of credentials).

2. Update the shell with the new variables:

source ~/.zshrc

3. Confirm that environmental variables are set:

echo $OPENAI_API_KEY
echo $OPENAI_API_VERSION
echo $OPENAI_API_BASE
echo $OPENAI_API_TYPE

4. Set up Conda environment:

git clone
conda create -n llana python=3.11.8
conda install jupyter
conda activate llana
## Note: {project_dir} is the path to where to your local directory
export PROJECT_DIR={project_dir}
conda env config vars set PYTHONPATH=${PYTHONPATH}:${PROJECT_DIR}
conda env config vars set PROJECT_DIR=${PROJECT_DIR}
conda deactivate
conda activate llana

5. Install requirements:

pip install -r requirements.txt

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2. Reproducing Results

To reproduce results, execute any of the shell experimental shell scripts:

• To run benchmark: run_custom_baselines.sh
• To evaluate the surrogate model: run_evaluate_dis_sm_openai.sh (discriminative SM)

3. Experimental logs

To check the experimal logs

exp_evaluate_sm/logs

exp_custom/openai