Presentation
DiffDEG: Diffusion-Enhanced Design Evolution Graph Representation Learning for Post-Layout Optimization
DescriptionPost-layout optimization is a critical step in modern chip design. However, existing Machine Learning (ML)-assisted methods struggle to capture the cross-stage reasoning dependencies that underlie the optimization challenges. While large language models (LLMs) excel at semantic reasoning, the lack of structural and physical awareness limits their effectiveness in post-layout optimization. To address these limitations, we propose DiffDEG, a diffusion-enhanced, reasoning-aware foundation model that bridges LLM-based semantic reasoning with circuit-level structural and physical representations. DiffDEG reformulates the conventional timing graph into a Design Evolution Graph (DEG), enabling cross-stage reasoning through text-annotated netlists. By leveraging directional diffusion and self-supervised pretraining, DiffDEG jointly interprets semantic, timing, and physical information, forming a unified representation adaptable to diverse optimization tasks. Experimental results show that DiffDEG consistently enhances optimization outcomes across multiple optimization paradigms, achieving an average 12.5% performance improvement and 4x runtime speedup over commercial tools.
Event Type
Research Manuscript
TimeTuesday, July 281:42pm - 1:55pm PDT
LocationMtg Room 101A