  Circuit design is complicated and requires extensive domain-specific
expertise. One major obstacle stuck on the way to hardware agile development is
the considerably time-consuming process of accurate circuit quality evaluation.
To significantly expedite the circuit evaluation during the translation from
behavioral languages to circuit designs, we formulate it as a
Program-to-Circuit problem, aiming to exploit the representation power of graph
neural networks (GNNs) by representing C/C++ programs as graphs. The goal of
this work is four-fold. First, we build a standard benchmark containing 40k
C/C++ programs, each of which is translated to a circuit design with actual
hardware quality metrics, aiming to facilitate the development of effective
GNNs targeting this high-demand circuit design area. Second, 14
state-of-the-art GNN models are analyzed on the Program-to-Circuit problem. We
identify key design challenges of this problem, which should be carefully
handled but not yet solved by existing GNNs. The goal is to provide
domain-specific knowledge for designing GNNs with suitable inductive biases.
Third, we discuss three sets of real-world benchmarks for GNN generalization
evaluation, and analyze the performance gap between standard programs and the
real-case ones. The goal is to enable transfer learning from limited training
data to real-world large-scale circuit design problems. Fourth, the
Program-to-Circuit problem is a representative within the Program-to-X
framework, a set of program-based analysis problems with various downstream
tasks. The in-depth understanding of strength and weaknesses in applying GNNs
on Program-to-Circuit could largely benefit the entire family of Program-to-X.
Pioneering in this direction, we expect more GNN endeavors to revolutionize
this high-demand Program-to-Circuit problem and to enrich the expressiveness of
GNNs on programs.
