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GNN(graph neural network (GNN)-based Path-Aware multi-view circuit learning), a novel GNN framework that learns precise, data-driven delay predictions by synergistically fusing three complementary views of circuit structure.
LevelSyn is a novel physical-aware logic synthesis framework that integrates hierarchical representation learning with a wirelength-driven optimization engine that significantly outperforms state-of-the-art methods on the EPFL benchmark suite.
In the controlled operator study, the HINT-mediated route shows better observed convergence and avoids the severe implementation-quality degradation seen in several direct-generation results.
DeepSeq3 is introduced, a novel hierarchical framework that abstracts circuits into a two-level representation: fine-grained combinational subgraphs partitioned by flip-flops (FFs) and a high-level Super-Node Graph (SNG) that models the register-transfer structure.
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