MIMIC: Machine Intelligence for Scalable Generation of Synthetic Timing Cone Datasets

Many machine learning (ML)-based approaches have been proposed to predict and optimize timing. However, the effectiveness of these techniques is often limited by the lack of large-scale, diverse, and realistic datasets-particularly those that reflect the structural and timing complexities of industry-scale designs. In this work, we introduce MIMIC (Machine intelligence for scalable generation of synthetic timing cone datasets), a methodology for generating high-quality synthetic timing cones that mimic real circuit netlist topologies and timing characteristics. The framework synthesizes a diverse dataset using a three-stage ML pipeline: (1) timing cone shape generation, (2) node type prediction for technology mapping, and (3) edge prediction to model internal cone connectivity. MIMIC produces synthetic yet realistic timing cones for a large-scale netlist within a few minutes. We evaluate the dataset using quantitative statistical EDA and ML world metrics for realism and structural diversity. We also demonstrate the generalizability of the MIMIC dataset for an ML-based timing prediction task.