Addressing Continuity and Expressivity Limitations in Differentiable Physical Optimization: A Case Study in Gate Sizing

Abstract

Differentiable optimization is popular for its efficiency and explainability. However, it faces limitations due to its reliance on continuous formulations and constraints on objective expressivity. To address these challenges, we propose a framework combining differentiable methods with gradient clipping and calibration strategies to ensure efficient and targeted optimization. Gate sizing, a key challenge in chip PPA optimization, exemplifies all the challenges with its discrete nature and objective complexity. Applying our proposed differentiable framework to gate sizing, we outperform top contestants in the 2024 ICCAD CAD gate sizing contest in overall quality scores and runtime, with excellent and balanced performance on all important evaluation metrics.