Presentation
Early Design Stage Thermal Prediction and Optimization: Machine-Learning Driven Approach
DescriptionAs semiconductor technologies continue to scale toward advanced nodes, the sharp increase in transistor density has led to a substantial rise in on-chip power density, making thermal effects a first-order design concern. Traditional thermal mitigation techniques (e.g., thermal-aware coarse-grained floor-planning, thermal-aware (post-route) cell adjustment, and structural cooling enhancements)
suffer from thermal prediction inaccuracy or incur high fabrication cost, limiting their practical applicability to modern SoC designs. To overcome the limitation, in this work, we present an ML-based early-stage thermal prediction and mitigation framework that enables proactive thermal management in the course of physical design process. Precisely, our approach (1) predicts first the power density map which is the underlying source of thermodynamic behavior during the global placement stage using machine learning models, and then (2) accurately estimates the steady-state thermal map through a physics-guided thermal interpolation. The predicted temperature is subsequently leveraged by (3) an ML-model based optimization engine that adjusts the placement solution to minimize thermal hotspots without timing degradation. Experimental results demonstrate that our proposed model achieves 49.0% and 34.9% accuracy improvement in power density and thermal prediction, respectively, compared to tool estimation. When applied to thermal-aware placement optimization, the framework successfully reduces the maximum chip temperature by 9.97◦C while maintaining equivalent timing and area. These results confirm the effectiveness of our proposed early-stage thermal modeling and optimization framework in improving thermal reliability for modern power-hungry SoCs.
suffer from thermal prediction inaccuracy or incur high fabrication cost, limiting their practical applicability to modern SoC designs. To overcome the limitation, in this work, we present an ML-based early-stage thermal prediction and mitigation framework that enables proactive thermal management in the course of physical design process. Precisely, our approach (1) predicts first the power density map which is the underlying source of thermodynamic behavior during the global placement stage using machine learning models, and then (2) accurately estimates the steady-state thermal map through a physics-guided thermal interpolation. The predicted temperature is subsequently leveraged by (3) an ML-model based optimization engine that adjusts the placement solution to minimize thermal hotspots without timing degradation. Experimental results demonstrate that our proposed model achieves 49.0% and 34.9% accuracy improvement in power density and thermal prediction, respectively, compared to tool estimation. When applied to thermal-aware placement optimization, the framework successfully reduces the maximum chip temperature by 9.97◦C while maintaining equivalent timing and area. These results confirm the effectiveness of our proposed early-stage thermal modeling and optimization framework in improving thermal reliability for modern power-hungry SoCs.
Event Type
Research Manuscript
TimeMonday, July 274:18pm - 4:30pm PDT
LocationMtg Room 202AB