AI RESEARCH
Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
arXiv CS.LG
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ArXi:2508.18730v2 Announce Type: replace Estimating the quality of register transfer level (RTL) designs is crucial in the electronic design automation (EDA) workflow, as it enables instant feedback on key performance metrics like area and delay without the need for time-consuming logic synthesis. While recent approaches have leveraged large language models (LLMs) to derive embeddings from RTL code and achieved promising results, they overlook the structural semantics essential for accurate quality estimation.