用知识图谱增强大模型，让其读懂长篇电路设计文档
ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications
- 将电路说明转为领域知识图谱，支持复杂逻辑推理
- 在工业级数据集上平均提升34.59%，最高达72.73%
- 适合需要处理长文本电路设计的硬件工程师和研究者
尽管大语言模型在集成电路开发自动化方面潜力巨大，但受限于有限的上下文窗口，实际部署困难。现有扩展上下文的方法难以对冗长复杂的电路规格进行有效语义建模与多跳推理。为此，我们提出ChipMind，一种专为长篇电路规格设计的知识图谱增强推理框架。该框架首先通过电路语义感知的知识图谱构建方法，将电路规格转化为领域专用知识图谱ChipKG；随后采用ChipKG增强推理机制，结合信息论自适应检索以动态追踪逻辑依赖，并通过意图感知语义过滤剔除无关噪声，有效平衡了检索的完整性和精确性。在工业级规格推理基准上评估，ChipMind显著优于现有最先进基线，平均提升34.59%（最高达72.73%）。该框架弥合了学术研究与实际工业中大模型辅助硬件设计应用之间的关键差距。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough multi-hop reasoning over extensive, intricate circuit specifications. To address this, we introduce ChipMind, a novel knowledge graph-augmented reasoning framework specifically designed for lengthy IC specifications. ChipMind first transforms circuit specifications into a domain-specific knowledge graph ChipKG through the Circuit Semantic-Aware Knowledge Graph Construction methodology. It then leverages the ChipKG-Augmented Reasoning mechanism, combining information-theoretic adaptive retrieval to dynamically trace logical dependencies with intent-aware semantic filtering to prune irrelevant noise, effectively balancing retrieval completeness and precision. Evaluated on an industrial-scale specification reasoning benchmark, ChipMind significantly outperforms state-of-the-art baselines, achieving an average improvement of 34.59% (up to 72.73%). Our framework bridges a critical gap between academic research and practical industrial deployment of LLM-aided Hardware Design (LAD).
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