Presentation
Static Timing Analysis (STA) Platform: An AI-Augmented Web System for Scalable Static Timing Analysis and Optimization
DescriptionThis presentation introduces an AI-augmented Static Timing Analysis (STA) analytics platform that shifts timing closure from a reactive debugging process to a proactive, predictive, and data-driven workflow. The platform automatically ingests and structures large-scale STA datasets, enabling interactive visualization of multi-corner and multi-mode timing behavior. Machine-learning models are applied to predict slack evolution, identify high-risk paths and corners, detect timing anomalies, and estimate ECO effectiveness prior to implementation. By integrating deterministic STA results with AI-driven insights, the approach substantially reduces manual log analysis, accelerates timing closure cycles, and improves sign-off confidence. This methodology allows engineers to quickly prioritize critical timing issues, reduce regression risk, and efficiently scale STA analysis as design size and complexity continue to increase.
Event Type
Engineering Poster
TimeWednesday, July 293:00pm - 4:00pm PDT
LocationDAC Pavilion, Exhibit Floor