Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI

Hang Yang; Yusheng Hu; Yong Liu; Cong; Hao

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arXiv · 2506.15907

Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI

Hang Yang·Yusheng Hu·Yong Liu·Cong·Hao

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Abstract

Accurate graph similarity is critical for knowledge transfer in VLSI design, enabling the reuse of prior solutions to reduce engineering effort and turnaround time. We propose Pieceformer, a scalable, self-supervised similarity assessment framework, equipped with a hybrid message-passing and graph transformer encoder. To address transformer scalability, we incorporate a linear transformer backbone and introduce a partitioned training pipeline for efficient memory and parallelism management. Evaluations on synthetic and real-world CircuitNet datasets show that Pieceformer reduces mean absolute error (MAE) by 24.9% over the baseline and is the only method to correctly cluster all real-world design groups. We further demonstrate the practical usage of our model through a case study on a partitioning task, achieving up to 89% runtime reduction. These results validate the framework's effectiveness for scalable, unbiased design reuse in modern VLSI systems.

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BibTeX RIS

Hang Yang, Yusheng Hu, Yong Liu, Cong, Hao. 2025-06-18. Pieceformer: Similarity-Driven Knowledge Transfer via Scalable Graph Transformer in VLSI. https://arxiv.org/abs/2506.15907

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