Modern ASIC design is becoming increasingly complex, driving up design costs while limiting productivity gains from existing EDA tools. Despite decades of progress, current tools rely on fixed heuristics and offer limited control via tool hyperparameters, requiring extensive manual tuning to achieve an acceptable quality of results (QoR). While prior work has explored learning-based optimization and design-specific hyperparameter tuning, these approaches operate within the constraints of static tool algorithm implementations and do not adapt the underlying algorithms to individual designs. To address this limitation, we introduce the concept of design-adaptive EDA tooling, in which the internal algorithms of EDA tools are automatically specialized to the characteristics of a given design. We instantiate this paradigm through GR-Evolve, a code evolution framework that leverages an agentic large language model (LLM) to iteratively modify global routing source code using QoR-driven feedback. The framework equips the LLM with persistent contextual knowledge of open-source global routers along with an integrated toolchain for QoR evaluation within the OpenROAD infrastructure. We evaluate GR-Evolve across seven benchmark designs across three technology nodes and demonstrate up to 8.72% reduction in post-detailed-routing wirelength over existing baseline routers, highlighting the potential of LLM-driven EDA code evolution for design-adaptive global routing.

翻译：现代ASIC设计复杂性日益增加，不仅推高了设计成本，还限制了现有EDA工具的生产力提升。尽管经过数十年发展，当前工具仍依赖固定启发式算法，通过工具超参数提供的控制能力有限，需要大量手动调优才能获得可接受的结果质量（QoR）。虽然先前工作已探索了基于学习的优化和设计特定超参数调优，但这些方法受限于静态工具算法实现，无法根据具体设计调整底层算法。为解决这一局限，我们提出设计自适应EDA工具的概念，使EDA工具的内部算法能自动针对给定设计的特性进行特化。我们通过GR-Evolve实现这一范式——该代码演化框架利用智能体大语言模型（LLM），基于QoR驱动的反馈迭代修改全局布线源代码。该框架为LLM提供开源全局布线器的持久上下文知识，以及集成OpenROAD基础设施的QoR评估工具链。我们在涵盖三种工艺节点的七个基准设计上评估GR-Evolve，结果表明相较现有基线布线器，详细布线后线长最多减少8.72%，凸显了LLM驱动的EDA代码演化在设计自适应全局布线中的潜力。