Statistical static timing analysis via modern optimization lens: I. Histogram–based approach

Statistical static timing analysis (SSTA) is studied from the mathematical optimization point of view. We give two formulations of the problem of finding the critical path delay distribution that were not known before: (i) a formal mathematical formulation of the SSTA problem using Binary–Integer Programming and (ii) a practical formulation using Geometric Programming. For simplicity, we use histogram approximation of the distributions. Scalability of the approaches are studied and possible generalizations are discussed.

Set up:

pip install -r requirements.txt
pip install .
python3 setup.py clean

For more details, please see:

The preprint is available at arXiv:2211.02981

Full documentation at: docs/_build/html

Examples at: examples/:

• Jupyter Notebook that shows BIP approach.ipynb
• Jupyter Notebook that shows GP approach
• A simple two input gate via BIP
• A simple two input gate via GP

How to cite

@misc{Bosak2023,
  doi = {10.48550/ARXIV.2211.02981},  
  url = {https://arxiv.org/abs/2211.02981},
  author = {Bosak, Adam and Mishagli, Dmytro and Marecek, Jakub},
  title = {Statistical timing analysis via modern optimization lens: I. Histogram–based approach},
  publisher = {arXiv},
  year = {2023},
}