Create PyTorch-Geometric graph datasets from GDSII files for ML training.
npx skills add https://github.com/ShenShan123/agent-r2g --skill def-graph
Please help me install this Agent Skill. Skill: def-graph Source: https://github.com/ShenShan123/agent-r2g/tree/main/r2g-skills/def-graph Command: npx skills add https://github.com/ShenShan123/agent-r2g --skill def-graph
This Skill automates the creation of training-ready PyTorch-Geometric graph datasets from GDSII files generated during RTL-to-GDS design signoff, saving time and reducing errors in the data preparation process.
Use the def-graph skill to create a graph dataset from the 'final.gds' file and the 'sky130.lib' liberty file in the 'design_cases/your_design' directory.
High-intent search queries and answers about installing and using this skill.
To convert GDSII files to PyTorch-Geometric datasets, you can automate the parsing of physical design files like DEF, LEF, and liberty to generate training-ready graph data. This process directly creates graph datasets with features and labels for machine learning models.
You can generate five different graph topologies from physical design files to support diverse machine learning training scenarios. These topologies are extracted from RTL-to-GDS design signoff outputs like GDSII and SPEF files.
Yes, creating graph datasets from GDSII files requires PyTorch, torch_geometric, pandas, and OpenROAD tools. These dependencies are necessary to parse the physical design inputs and structure them into training-ready graph data.
Yes, you can use DEF, LEF, liberty, and SPEF files to train ML models for IC design verification. The dataset creation process transforms these physical design files into PyTorch-Geometric graphs with associated features and labels.
The best way to automate data preparation in RTL-to-GDS flows is by converting signoff GDSII files directly into PyTorch-Geometric graph datasets. This reduces manual errors and saves time by structuring physical design data for immediate model training.
You need to create graph datasets from GDSII files when preparing data for machine learning models applied to IC design verification workflows. This is particularly useful during physical design and signoff processes to structure complex layout data for training.
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