Presentation
InsightAlign: A Transferable Physical Design Recipe Recommender Based on Design Insights
SessionSmart Circuits, Smarter Algorithms: AI-Driven Innovations in Circuit Modeling and Optimization
DescriptionPhysical design tools have complex workflows with many different ways of optimizing power, performance and area (PPA) out of a large number of options and hyperparameters in different engines and functionalities. Black box optimization techniques are widely adapted to automate quality-of-result (QoR) exploration. Such exploration often proves impractical in real-world customer environments due to high computational demands, lengthy exploration cycles, and the need for large parallel jobs. To reduce the exploration space for viable compute resource requirement, we propose a novel design methodology to enable transferable learning by incorporating design insights crafted on top of physical design experts' experience, and streamlining QoR exploration as a sequence generation task for best recipe selection, where we apply language model-inspired alignment techniques to learn the ranking of different recipe sets, enabling our model to generalize beyond known-good manually tuned expert design recipes. Extensive evaluations demonstrate our method's superior QoRs and runtime performance on unseen industrial designs and rigorous benchmarks.
Event Type
Research Manuscript
TimeWednesday, June 2511:15am - 11:30am PDT
Location3000, Level 3
AI
AI2: AI/ML Application and Infrastructure