发表机构
New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReDIL-GNN提出重综合域增量学习框架，通过RAI指数筛选适配需求，在电路GNN上实现有选择地更新模型，兼顾新域适配与旧域保持。
AI 中文摘要
逻辑重综合在保持电路功能的同时改变门词汇、拓扑和结构统计，从而在不改变任务标签的情况下为电路图神经网络（GNN）引入域偏移。为研究这一设置，我们提出了ReDIL-GNN，一种重综合域增量学习框架，该框架在新型综合风格出现时适配固定的预测或表示头，并评估对所有先前观察域的保持能力。由于并非每次偏移都应盲目适配，ReDIL-GNN进一步引入了重综合适配性指数（RAI），这是一种适配前评分，结合了适配需求、源等价可恢复性、结构覆盖和更新兼容性。我们使用任务原生指标评估监督式硬件安全任务和表示学习模型，其中分类器采用分类指标，嵌入模型采用源等价检索指标，并将朴素微调与LwF、Online EWC、MAS、ER、A-GEM、DER++、ER+LwF和等价引导重放进行比较。在所研究的流程中，RAI将不支持的偏移与有前景的更新区分开来，范围从结构未覆盖的GNN-RE ABC重写偏移的0.001到最佳仅原始GNN-RE适配案例的0.824。在实践中，ReDIL-GNN将重综合感知的电路学习转变为部署控制循环：RAI在每次更新前筛选新的综合流程，指导是复用当前模型、应用保持感知适配，还是推迟适配直到偏移得到更好支持。
英文摘要
Logic resynthesis preserves circuit functionality while changing gate vocabulary, topology, and structural statistics, creating domain shift for circuit graph neural networks (GNNs) without changing task labels. To study this setting, we introduce ReDIL-GNN, a resynthesis domain-incremental learning framework that adapts a fixed prediction or representation head as new synthesis styles arrive and evaluates retention on all previously observed domains. Because not every shift should be adapted blindly, ReDIL-GNN further introduces the Resynthesis Adaptability Index (RAI), a pre-adaptation score that combines adaptation need, source-equivalence recoverability, structural coverage, and update compatibility. We evaluate supervised hardware-security tasks and representation-learning models using task-native metrics for classifiers and source-equivalence retrieval metrics for embedding models, comparing naive fine-tuning with LwF, Online EWC, MAS, ER, A-GEM, DER++, ER+LwF, and equivalence-guided replay. Across the studied pipelines, RAI separates unsupported shifts from promising updates, ranging from 0.001 for a structurally uncovered GNN-RE ABC-rewrite shift to 0.824 for the best original-only GNN-RE adaptation case. In practice, ReDIL-GNN turns resynthesis-aware circuit learning into a deployment control loop: RAI screens each new synthesis flow before update, guiding whether to reuse the current model, apply retention-aware adaptation, or defer adaptation until the shift is better supported.
Comments12 pages
