Presentation
Generative Model Based Standard Cell Timing Library Characterization
DescriptionAccurate cell timing characterization is essential, on which static timing analysis relies to verify timing performance and ensure design robustness across various PVT conditions (corners). The corner explosion in modern design amplifies the efficiency and scalability challenge for accurate characterization. However, the conventional characterization approach of SPICE simulation alone becomes prohibitively expensive due to the increasing computational complexity and the amount of characterized data. In this paper, we view the characterization problem from a generative modeling perspective to tackle the efficiency and scalability challenge. With a hybrid of generative adversarial network (GAN) and autoencoder, our generative model learns and generalizes among various timing arcs and corners. Experimental results demonstrate that the proposed framework achieves high accuracy and extensibility while reducing the runtime significantly.
Event Type
Research Manuscript
TimeMonday, June 2311:00am - 11:15am PDT
Location3004, Level 3
EDA
EDA3: Timing Analysis and Optimization