Title: UniRTL: Unifying Code and Graph for Robust RTL Representation Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: RTL Representation Learning, Multimodal Learning

Abstract: Developing effective representations for register transfer level (RTL) designs is crucial for accelerating the hardware design workflow.
Existing approaches, however, typically rely on a single data modality, either the RTL code or its associated graph-based representation, limiting the expressiveness and generalization ability of the learned representations. 
Particularly, graph-related methods often adopt data flow or register-level sub-circuits, both of which capture only partial information and thus provide an incomplete view of the design.
In contrast, the control data flow graph (CDFG) offers a more comprehensive structural representation that preserves complete information, while the code modality explicitly encodes semantic and functional information.
We argue that integrating these complementary modalities is essential for a thorough understanding of RTL designs.
To this end, we propose UniRTL, a multimodal pretraining framework that learns unified RTL representations by jointly leveraging code and CDFG.
UniRTL achieves fine-grained alignment between code and graph through mutual masked modeling and employs a hierarchical training strategy that incorporates a pretrained graph-aware tokenizer and staged alignment of text (i.e., functional summary) and code prior to graph integration.
We evaluate UniRTL on two downstream tasks, performance prediction and code retrieval, under multiple settings. Experimental results show that UniRTL consistently outperforms prior methods, establishing it as a more robust and powerful foundation for advancing hardware design automation.