Presentation
TEA-GNN: Technology Node Exploration Acceleration via End-of-Flow Metric Prediction
DescriptionModern designs demand stringent performance metrics to sustain innovation. Advanced technological nodes offer potential enhancements, but the relationship between node size, speed, and power consumption is intricate, making node selection challenging. To address these challenges, we propose TEA-GNN, a framework leveraging Graph Neural Networks (GNNs) and adapters for rapid assessment of power and slack performance across multiple technology nodes. TEA-GNN uses a two-stage prediction process: first predicting timing metrics, and then using these inputs for power prediction, enhancing accuracy. Experimental results on real-world designs demonstrate significant runtime improvement and lower error compared to commercial tools, reducing design iteration time and enabling faster time-to-market.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby