Transferable Presynthesis PPA Estimation for RTL Designs With Data Augmentation Techniques

In modern VLSI design flow, evaluating the quality of register-transfer level (RTL) designs involves time-consuming logic synthesis using electronic design automation tools, a process that often slows down early optimization. While recent machine learning (ML) solutions offer some advancements, they typically struggle with maintaining high accuracy across any given RTL design. In this work, we propose an innovative transferable presynthesis power, performance, and area (PPA) estimation framework named MasterRTL. It first converts the hardware description language code to a new bit-level design representation named the simple operator graph (SOG). By only adopting single-bit simple operators, this SOG proves to be a general representation that unifies different design types and styles. The SOG is also more similar to the target gate-level netlist, reducing the gap between the RTL representation and netlist. In addition to the new SOG representation, MasterRTL proposes new ML methods for the RTL-stage modeling of timing, power, and area separately. Compared with the state-of-the-art solutions, the experiment on a comprehensive dataset with 90 different designs shows accuracy improvement by 0.33, 0.22, and 0.15 in correlation for total negative slack (TNS), worst negative slack (WNS), and power, respectively. Besides the prediction of the synthesis results, MasterRTL also excels in accurately predicting layout-stage PPA based on the RTL designs and in adapting across different technology nodes and process corners. Furthermore, we investigate two effective data augmentation techniques: 1) a graph generation method and 2) a large language model (LLM)-based approach. Our results validate the effectiveness of the generated RTL designs in mitigating the data shortage challenges.