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).
@article{arxiv.2512.05371,
title = {ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications},
author = {Changwen Xing and SamZaak Wong and Xinlai Wan and Yanfeng Lu and Mengli Zhang and Zebin Ma and Lei Qi and Zhengxiong Li and Nan Guan and Zhe Jiang and Xi Wang and Jun Yang},
journal= {arXiv preprint arXiv:2512.05371},
year = {2025}
}