大语言模型在数字电子设计自动化（EDA）中的应用：从生成到编排的角色转变视角
LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration
- The University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文探讨LLMs在EDA领域的应用，定义生成器、智能体、编排器三个层级角色，指出当前方案存在语法陷阱且难以扩展至工业设计，提出需转向标准化、感知物理规律的编排器以优化硬件设计。
AI中文摘要：
电子设计自动化（EDA）通过 successive generations of tooling 逐步实现综合、优化与验证的自动化，提升了工程生产力。大语言模型（LLMs）延续这一趋势，支持从设计意图直接转换为硬件实现。在多数 EDA 文献中，基于 LLM 的解决方案通常辅助孤立的设计阶段或任务，却掩盖了能力涌现与系统扩展的驱动因素。本视角定义了三个层级角色以揭示能力积累方式：单次生成设计产物的生成器（Generator）、通过迭代工具反馈优化输出的智能体（Agent），以及跨 EDA 阶段协调决策的编排器（Orchestrator）。已发表系统的分析显示存在语法陷阱：模型被训练生成看似合理的代码，而非物理正确的硬件，加之工具碎片化与设计上下文丢失，导致决策对后续阶段的影响被掩盖。对三个角色的比较表明，当前方法难以扩展至工业设计，因此需转向标准化、感知物理规律的编排器，连接 EDA 流程中的工具与智能体，实现更可靠、易获取的硬件设计。
英文摘要：
Electronic design automation (EDA) has advanced engineering productivity through successive generations of tooling that progressively automate synthesis, optimisation, and verification. Large language models (LLMs) extend this trajectory by enabling direct translation from design intent to hardware implementations. In most of the EDA literature, LLM-based solutions are typically assisting siloed design stages or tasks, however this obscured the drivers by which capability emerges and systems scale. In this Perspective, we instead define three hierarchical roles that reveal how capability accumulates: a Generator that produces design artifacts in a single pass, an Agent that refines outputs through iterative tool feedback, and an Orchestrator that coordinates decisions across EDA-stages. Across published systems, this reveals a syntax trap in which models are trained to produce plausible code rather than physically correct hardware, compounded by fragmented tools and loss of design context that obscure how decisions affect later stages. Comparisons across the three roles show that current approaches struggle to scale to industrial designs, motivating a shift towards a standardised, physics-aware orchestrator that connects tools and agents across the EDA flow for more reliable and accessible hardware design.
