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ARXIV:2603.18581 · AI FOR HARDWARE DESIGN · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18581AI FOR HARDWARE DESIGNSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEHaotian Lu · Jincong Lu · Sachin Sachdeva · Sheldon X. -D. Tan · arXiv
A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods.
Opportunity summary
Pain A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods. While conventional numerical approaches can deliver highly accurate results, they often incur…
With the advent of system-in-package (SiP) chiplet-based design and heterogeneous 2.5D/3D integration, thermal-induced warpage has become a critical reliability concern. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively high computational costs, limiting their scalability for complex chiplet-package systems.…
AI for Hardware Design moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods.
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10.48550/arXiv.2603.18581A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods.
Abstract
With the advent of system-in-package (SiP) chiplet-based design and heterogeneous 2.5D/3D integration, thermal-induced warpage has become a critical reliability concern. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively high computational costs, limiting their scalability for complex chiplet-package systems. In this paper, we present WarPGNN, an ef- ficient and accurate parametric thermal warpage analysis framework powered by Graph Neural Networks (GNNs). By operating directly on graphs constructed from the floorplans, WarPGNN enables fast warpage-aware floorplan exploration and exhibits strong transfer- ability across diverse package configurations. Our method first en- codes multi-die floorplans into reduced Transitive Closure Graphs (rTCGs), then a Graph Convolution Network (GCN)-based encoder extracts hierarchical structural features, followed by a U-Net inspired decoder that reconstructs warpage maps from graph feature embed- dings. Furthermore, to address the long-tailed pattern of warpage data distribution, we developed a physics-informed loss and revised a message-passing encoder based on Graph Isomorphic Network (GIN) that further enhance learning performance for extreme cases and expressiveness of graph embeddings. Numerical results show that WarPGNN achieves more than 205.91x speedup compared with the 2-D efficient FEM-based method and over 119766.64x acceleration with 3-D FEM method COMSOL, respectively, while maintaining comparable accuracy at only 1.26% full-scale normalized RMSE and 2.21% warpage value error. Compared with recent DeepONet-based model, our method achieved comparable prediction accuracy and in- ference speedup with 3.4x lower training time. In addition, WarPGNN demonstrates remarkable transferability on unseen datasets with up to 3.69% normalized RMSE and similar runtime.
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PROBLEM
A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively h...
METHOD
With the advent of system-in-package (SiP) chiplet-based design and heterogeneous 2.5D/3D integration, thermal-induced warpage has become a critical reliability concern. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. While conventional numerical approaches can deliver highly accurate results, they often incur prohib- itively high computational costs, limiting their scalability for complex chiplet-package systems. Code...
WHY NOW
AI for Hardware Design moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
Numerical results show that WarPGNN achieves more than 205.91x speedup compared with the 2-D efficient FEM-based method
Directly stated in abstract with specific numeric comparison
partial
over 119766.64x acceleration with 3-D FEM method COMSOL
Directly stated in abstract with specific numeric comparison
partial
while maintaining comparable accuracy at only 1.26% full-scale normalized RMSE
Directly stated in abstract with specific accuracy metric
partial
2.21% warpage value error
Directly stated in abstract with specific error metric
partial
Compared with recent DeepONet-based model, our method achieved comparable prediction accuracy and inference speedup with 3.4x lower training time
Directly stated in abstract with specific comparison to alternative method
partial
WarPGNN demonstrates remarkable transferability on unseen datasets with up to 3.69% normalized RMSE
Directly stated in abstract with specific transferability metric
partial
to address the long-tailed pattern of warpage data distribution, we developed a physics-informed loss
Directly stated in abstract as a key methodological component
partial
revised a message-passing encoder based on Graph Isomorphic Network (GIN) that further enhance learning performance for extreme cases
Directly stated in abstract as a key methodological component
partial
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A physics-informed Graph Neural Network framework that accelerates thermal warpage analysis for chiplet-based designs by over 200x compared to traditional FEM methods.
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