Annotating Slack Directly on Your Verilog: Fine-Grained RTL Timing Evaluation for Early Optimization
Wenji Fang, Shang Liu, Hongce Zhang, Zhiyao Xie
摘要
In digital IC design, compared with post-synthesis netlists or layouts, the early register-transfer level (RTL) stage offers greater optimization flexibility for both designers and EDA tools. However, timing information is typically unavailable at this early stage. Some recent machine learning (ML) solutions propose to predict the total negative slack (TNS) and worst negative slack (WNS) of an entire design at the RTL stage, but the fine-grained timing information of individual registers remains unavailable. In this work, we address the unique challenges of RTL timing prediction and introduce our solution named RTL-Timer. To the best of our knowledge, this is the first fine-grained general timing estimator applicable to any given design. RTL-Timer explores multiple promising RTL representations and proposes customized loss functions to capture the maximum arrival time at register endpoints. RTL-Timer's finegrained predictions are further applied to guide optimization in a standard synthesis flow. The average results on unknown test designs demonstrate a correlation above 0.89, contributing around 3% WNS and 10% TNS improvement after optimization.
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引用它的顶会 Paper7
- NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed GraphWenji Fang, Wenkai Li, Shang Liu, Yao Lu 等DAC 2025 · 被引用 10 次
- VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog CodeRaghu Vamshi Hemadri, Jitendra Bhandari, Andre Nakkab, Johann Knechtel 等NeurIPS 2025 · 被引用 9 次
- SynCircuit: Automated Generation of New Synthetic RTL Circuits Can Enable Big Data in CircuitsShang Liu, Jing Wang, Wenji Fang, Zhiyao XieDAC 2025 · 被引用 1 次
- SynC-LLM: Generation of Large-Scale Synthetic Circuit Code with Hierarchical Language ModelsShang Liu, Yao Lu, Wenji Fang, Jing Wang 等EMNLP 2025
- Bridging Layout and RTL: Knowledge Distillation based Timing PredictionMingjun Wang, Yihan Wen, Bin Sun, Jianan Mu 等ICML 2025
它引用的顶会 Paper4
- A timing engine inspired graph neural network model for pre-routing slack predictionZizheng Guo, Mingjie Liu, Jiaqi Gu, Shuhan Zhang 等DAC 2022 · 被引用 121 次
- Accurate timing prediction at placement stage with look-ahead RC networkXu He, Zhiyong Fu, Yao Wang, Chang Liu 等DAC 2022 · 被引用 43 次
- Restructure-Tolerant Timing Prediction via Multimodal FusionZiyi Wang, Siting Liu, Yuan Pu, Song Chen 等DAC 2023 · 被引用 33 次
- SNS's not a synthesizer: a deep-learning-based synthesis predictorCeyu Xu, Chris Kjellqvist, Lisa Wu WillsISCA 2022 · 被引用 23 次
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