Presentation
Self-Attention To Operator Learning-based 3D-IC Thermal Simulation
SessionSmart Circuits, Smarter Algorithms: AI-Driven Innovations in Circuit Modeling and Optimization
DescriptionThermal management in 3D ICs faces challenges due to higher power densities. Traditional PDE-based simulation methods are accurate but too slow for iterative design. Machine learning methods like FNO offer alternatives but struggle with high-frequency information loss and reliance on high-fidelity training data. We propose SAU-FNO, integrating self-attention and U-Net with FNO to capture long-range dependencies and local features, enhancing high-frequency modeling. Transfer learning further reduces high-fidelity data needs and accelerates training. SAU-FNO achieves state-of-the-art thermal prediction and an 842× speedup over conventional FEM methods, offering an efficient solution for advanced 3D IC thermal management.
Event Type
Research Manuscript
TimeWednesday, June 2511:30am - 11:45am PDT
Location3000, Level 3
AI
AI2: AI/ML Application and Infrastructure