ArtNet生成逼真电路网表，提升ML和DTCO优化效率。
ArtNet: Hierarchical Clustering-Based Artificial Netlist Generator for ML and DTCO Application
- 基于分层聚类生成具有真实拓扑特征的电路网表。
- 在CNN预测中提升F1分数0.16，实现97.94%的PPA匹配度。
- 适合需要数据增强的ML模型训练与DTCO设计探索者。
在先进制程节点下，功耗、性能与面积（PPA）优化变得高度复杂且具挑战性。机器学习（ML）与设计-工艺协同优化（DTCO）提供了有效缓解方案，但受限于训练数据多样性不足及设计流程周转时间（TAT）过长。本文提出ArtNet，一种新型人工网表生成器，可复制关键拓扑特征，提升机器学习模型泛化能力，并支持更广的设计空间探索。通过生成更贴近目标参数的真实人工数据集，ArtNet实现了更高效的PPA优化与流程探索。在基于CNN的驱动电阻值（DRV）预测任务中，使用ArtNet数据增强后F1得分提升0.16；在DTCO场景中，生成的微型电路（mini-brains）与全规模模块设计的PPA匹配度达97.94%，表明其设计指标高度一致。
原文摘要 · Abstract (English)
In advanced nodes, optimization of power, performance and area (PPA) has become highly complex and challenging. Machine learning (ML) and design-technology co-optimization (DTCO) provide promising mitigations, but face limitations due to a lack of diverse training data as well as long design flow turnaround times (TAT). We propose ArtNet, a novel artificial netlist generator designed to tackle these issues. Unlike previous methods, ArtNet replicates key topological characteristics, enhancing ML model generalization and supporting broader design space exploration for DTCO. By producing realistic artificial datasets that moreclosely match given target parameters, ArtNet enables more efficient PPAoptimization and exploration of flows and design enablements. In the context of CNN-based DRV prediction, ArtNet's data augmentationimproves F1 score by 0.16 compared to using only the original (real) dataset. In the DTCO context, ArtNet-generated mini-brains achieve a PPA match up to 97.94%, demonstrating close alignment with design metrics of targeted full-scale block designs.
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