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

--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 . T o 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. The increasing complexity of VLSI systems--driven by advanced packaging technologies, shrinking technology nodes, and rapid product cycles--has placed enormous pressure on modern semiconductor design workflows. Meanwhile, tasks such as synthesis, placement, routing, and verification remain highly iterative, computationally expensive, and dependent on deep domain expertise. As a result, there is a growing need for automated methods that can effectively reuse knowledge from existing, optimized designs to accelerate new design efforts.