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Circuit design is complicated and requires extensive domain-specificexpertise. One major obstacle stuck on the way to hardware agile development isthe considerably time-consuming process of accurate circuit quality evaluation.To significantly expedite the circuit evaluation during the translation frombehavioral languages to circuit designs, we formulate it as aProgram-to-Circuit problem, aiming to exploit the representation power of graphneural networks (GNNs) by representing C/C++ programs as graphs. The goal ofthis work is four-fold. First, we build a standard benchmark containing 40kC/C++ programs, each of which is translated to a circuit design with actualhardware quality metrics, aiming to facilitate the development of effectiveGNNs targeting this high-demand circuit design area. Second, 14state-of-the-art GNN models are analyzed on the Program-to-Circuit problem. Weidentify key design challenges of this problem, which should be carefullyhandled but not yet solved by existing GNNs. The goal is to providedomain-specific knowledge for designing GNNs with suitable inductive biases.Third, we discuss three sets of real-world benchmarks for GNN generalizationevaluation, and analyze the performance gap between standard programs and thereal-case ones. The goal is to enable transfer learning from limited trainingdata to real-world large-scale circuit design problems. Fourth, theProgram-to-Circuit problem is a representative within the Program-to-Xframework, a set of program-based analysis problems with various downstreamtasks. The in-depth understanding of strength and weaknesses in applying GNNson Program-to-Circuit could largely benefit the entire family of Program-to-X.Pioneering in this direction, we expect more GNN endeavors to revolutionizethis high-demand Program-to-Circuit problem and to enrich the expressiveness ofGNNs on programs.

Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation