Presentation
Machine Learning based Dynamic IR hotspot estimation for SoC Designs
DescriptionThis presentation introduces a machine learning (ML) model for rapid and accurate dynamic IR drop estimation in SoC designs. Traditional dynamic IR estimation methods are computationally expensive, with runtime complexity of N², hindering timely design finalization with good PPA metrics.
This work proposes an XGBoost regression-based ML model to predict vector-less dynamic IR using power and timing features.
Tested on two industrial SoCs in most recent process nodes, with over 1.5 million instances, the ML model achieved a 15x speedup.The model maintained accuracy with less than 1 mV Mean Square Error and a correlation coefficient of ~0.85. ROC accuracy of ~90.0 indicates close approximation of predicted vs. actual IR drop.
This work proposes an XGBoost regression-based ML model to predict vector-less dynamic IR using power and timing features.
Tested on two industrial SoCs in most recent process nodes, with over 1.5 million instances, the ML model achieved a 15x speedup.The model maintained accuracy with less than 1 mV Mean Square Error and a correlation coefficient of ~0.85. ROC accuracy of ~90.0 indicates close approximation of predicted vs. actual IR drop.
Event Type
Engineering Presentation
TimeMonday, June 232:00pm - 2:15pm PDT
Location2012, Level 2