Presentation
Using Big Data and ML techniques to triage timing violations
DescriptionIn the design automation industry, triaging timing violations is typically performed using a method that categorizes violations based on known path attributes such as startpoint, endpoint, slack magnitude, clocks, and data/clock path, and pin names. This approach, while effective, often results in an overwhelming number of violations within the same category especially in early design stages and dirty designs, making efficient triage challenging. I propose a novel method for triaging violations using machine learning techniques, specifically clustering. Instead of sorting paths by predefined attributes, this method groups them based on their similarity across these attributes. By changing the metrics considered by the clustering algorithm and configuring the algorithm itself, we can adjust the size and wideness of the clusters, allowing for controllability in cluster generation and providing better insights for the timing team. This clustering approach allows for the identification of relationships between paths that are not easily discernible through traditional methods or visual inspection.
Event Type
Engineering Poster
Networking
TimeMonday, June 235:00pm - 6:00pm PDT
LocationEngineering Posters, Level 2 Exhibit Hall