Scholarlink

Abstract <p>Accurate critical path delay estimation plays a vital role in reducing unnecessary routing iterations and identifying potentially unsuccessful design runs early in the flow. This study proposes an architecture that integrates graph representations derived from digital complex functional blocks netlist and design constraints, leveraging a Multi-head cross-attention mechanism. This architecture significantly improves the accuracy of critical path delay estimation compared to standard tools provided by the OpenROAD EDA. The mean absolute percentage error (MAPE) of the OpenRoad standard tool—openSTA is 12.60%, whereas our algorithm achieves a substantially lower error of 7.57%. A&#xa0;comparison of various architectures was conducted, along with an investigation into the impact of incorporating netlist-derived information.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging Graph Representations to Enhance Critical Path Delay Prediction in Digital Complex Functional Blocks Using Neural Networks

  • 期刊论文

  • 发表日期：

    2025年12月19日

查看全文
  • M. Dashiev,
  • N. Zheludkov,
  • I. Karandashev

摘要

Abstract

Accurate critical path delay estimation plays a vital role in reducing unnecessary routing iterations and identifying potentially unsuccessful design runs early in the flow. This study proposes an architecture that integrates graph representations derived from digital complex functional blocks netlist and design constraints, leveraging a Multi-head cross-attention mechanism. This architecture significantly improves the accuracy of critical path delay estimation compared to standard tools provided by the OpenROAD EDA. The mean absolute percentage error (MAPE) of the OpenRoad standard tool—openSTA is 12.60%, whereas our algorithm achieves a substantially lower error of 7.57%. A comparison of various architectures was conducted, along with an investigation into the impact of incorporating netlist-derived information.