DeepCell: Multiview Representation Learning for Post-Mapping Netlists

• URL: http://arxiv.org/abs/2502.06816v1
• Date: Wed, 05 Feb 2025 02:39:47 GMT
• Title: DeepCell: Multiview Representation Learning for Post-Mapping Netlists
• Abstract summary: DeepCell is a representation learning framework that integrates structural and functional insights from both PM netlists and AIGs.<n>At its core, DeepCell employs the novel Mask Circuit Modeling (MCM) mechanism, which refines PM netlist representations in a self-supervised manner.<n>DeepCell sets a new benchmark in PM netlist representation, outperforming existing methods in predictive accuracy and reconstruction fidelity.
• Score: 11.789986571844535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs. Existing approaches focus on intermediate representations like And-Inverter Graphs (AIGs), limiting their applicability to post-synthesis stages. We introduce DeepCell, a multiview representation learning framework that integrates structural and functional insights from both PM netlists and AIGs to learn rich, generalizable embeddings. At its core, DeepCell employs the novel Mask Circuit Modeling (MCM) mechanism, which refines PM netlist representations in a self-supervised manner using pretrained AIG encoders. DeepCell sets a new benchmark in PM netlist representation, outperforming existing methods in predictive accuracy and reconstruction fidelity. To validate its efficacy, we apply DeepCell to functional Engineering Change Orders (ECO), achieving significant reductions in patch generation costs and runtime while improving patch quality.

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