Presentation
Adaptive Graph Learning for Efficient Thermal Analysis of the Chiplet System under Interface Variations
DescriptionIn thermal analysis of a chiplet system, conventional numerical methods or machine learning-based surrogate models face tremendous challenges in computation cost and accuracy, especially in the presence of process and material variations. We propose Graph Neural Networks (GNNs) as a mathematical framework for efficient and robust thermal analysis with composite materials. By modeling each region and their thermal interactions as a graph, we continually adapt the GNN model under thermal interface variations. We validate our approach with numerical solutions and real thermal images from a crossbar unit, and demonstrate its speedup and accuracy in a 2.5D chiplet system.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security