Dynamic Circuit Synthesis via Generative Graph Neural Networks

Abstract

This paper presents a novel approach to digital circuit synthesis leveraging Generative Graph Neural Networks (GNNs). The core claim is that designing complex digital circuits is a significantly time-consuming and iterative process. To address this, we propose a system that utilizes a GNN to automatically generate circuit designs from high-level specifications. The system learns a mapping between abstract functional requirements and the corresponding digital circuit components, optimizing for performance and resource utilization. Our approach offers the potential for substantial efficiency gains in circuit design, moving away from manual, iterative methods. The system dynamically adapts to the specification, creating a truly dynamic synthesis process. The generated circuits are represented as graphs, allowing the GNN to effectively learn and generate complex network structures. This work contributes to the automation of circuit design, a crucial step towards accelerating innovation in digital hardware.