  The complexity of modern hardware designs necessitates advanced methodologies
for optimizing and analyzing modern digital systems. In recent times, machine
learning (ML) methodologies have emerged as potent instruments for assessing
design quality-of-results at the Register-Transfer Level (RTL) or Boolean
level, aiming to expedite design exploration of advanced RTL configurations. In
this presentation, we introduce an innovative open-source framework that
translates RTL designs into graph representation foundations, which can be
seamlessly integrated with the PyTorch Geometric graph learning platform.
Furthermore, the Verilog-to-PyG (V2PYG) framework is compatible with the
open-source Electronic Design Automation (EDA) toolchain OpenROAD, facilitating
the collection of labeled datasets in an utterly open-source manner.
Additionally, we will present novel RTL data augmentation methods (incorporated
in our framework) that enable functional equivalent design augmentation for the
construction of an extensive graph-based RTL design database. Lastly, we will
showcase several using cases of V2PYG with detailed scripting examples. V2PYG
can be found at \url{https://yu-maryland.github.io/Verilog-to-PyG/}.
