mlsanityData to 7 Oct 2026

MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design

arXiv 2311.08441 ↗First posted 14 Nov 2023cs.ARcited by 19

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Abstract (arXiv)

In modern VLSI design flow, the register-transfer level (RTL) stage is a critical point, where designers define precise design behavior with hardware description languages (HDLs) like Verilog. Since the RTL design is in the format of HDL code, the standard way to evaluate its quality requires time-consuming subsequent synthesis steps with EDA tools. This time-consuming process significantly impedes design optimization at the early RTL stage. Despite the emergence of some recent ML-based solutions, they fail to maintain high accuracy for any given RTL design. In this work, we propose an innovative pre-synthesis PPA estimation framework named MasterRTL. It first converts the HDL 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 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 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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2023.

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Citing papers per year: 2024: 2; 2025: 15; 2026: 2.

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  1. 2025DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysiscited by 21
  2. 2025SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoningcited by 16
  3. 2025DeepRTL2: A Versatile Model for RTL-Related Taskscited by 10
  4. 2025NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graphcited by 9
  5. 2024MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMscited by 7
  6. 2024The Graph's Apprentice: Teaching an LLM Low Level Knowledge for Circuit Quality Estimationcited by 3
  7. 2025VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Codecited by 3
  8. 2025DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretrainingcited by 2
  9. 2025ViTAD: Timing Violation-Aware Debugging of RTL Code using Large Language Modelscited by 1
  10. 2025Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learningcited by 1
  11. 2026TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilogcited by 1
  12. 2025E2ESlack: An End-to-End Graph-Based Framework for Pre-Routing Slack Predictioncited by 0
7 more
  1. 2025Machine Learning Framework for Early Power, Performance, and Area Estimation of RTLcited by 0
  2. 2025Lorecast: Layout-Aware Performance and Power Forecasting from Natural Languagecited by 0
  3. 2025RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Expertscited by 0
  4. 2025SynCircuit: Automated Generation of New Synthetic RTL Circuits Can Enable Big Data in Circuitscited by 0
  5. 2025BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reductioncited by 0
  6. 2025ParaGate: Parasitic-Driven Domain Adaptation Transfer Learning for Netlist Performance Predictioncited by 0
  7. 2026RTL-Sequencer: Towards Scalable RTL Timing Prediction with the Sequence-based Paradigmcited by 0