Harnessing GNNs for Robust Representation Learning in High-Level Synthesis

The efficient and timely optimization of microarchitecture for a target application is hindered by the long evaluation runtime of a design candidate, creating a serious burden. To tackle this problem, researchers have started using learning algorithms such as graph neural networks (GNNs) to accelerate the process by developing a surrogate of the target tool. However, challenges arise when developing such models for HLS tools due to the program's long dependency range and deeply coupled input program and transformations (i.e., pragmas). To address them, in this paper, we presentHARP(HierarchicalAugmentation forRepresentation withPragma optimization) with a novel hierarchical graph representation of the HLS design by introducing auxiliary nodes to include high-level hierarchical information about the design. Additionally,HARPdecouples the representation of the program and its transformations and includes a neural pragma transformer (NPT) approach to facilitate a more systematic treatment of this process. Our proposed graph representation and model architecture ofHARPnot only enhance the performance of the model and design space exploration based on it but also improve the model's transfer learning capability, enabling easier adaptation to new environments.