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ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications

Changwen Xing, SamZaak Wong, Xinlai Wan, Yanfeng Lu, Mengli Zhang, Zebin Ma, Lei Qi, Zhengxiong Li, Nan Guan, Zhe Jiang, Xi Wang, Jun Yang

TL;DR

This work addresses the challenge of performing deep, multi-hop reasoning over long industrial IC specifications, which typically exceed standard LLM context windows. It introduces ChipMind, a knowledge graph-augmented framework that constructs a Circuit KG (ChipKG) from specifications and uses adaptive, CSA-guided retrieval to enable iterative, verifiable reasoning. Evaluated on SpecEval-QA, ChipMind achieves state-of-the-art performance with an average improvement of 34.59% and up to 72.73% over strong baselines, and introduces Atomic-ROUGE as a factuality-focused evaluation metric with high correlation to human judgments. The approach demonstrates a practical path to deploying LLM-aided hardware design in industry by improving context coverage, reasoning reliability, and evaluation rigor.

Abstract

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).

ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications

TL;DR

This work addresses the challenge of performing deep, multi-hop reasoning over long industrial IC specifications, which typically exceed standard LLM context windows. It introduces ChipMind, a knowledge graph-augmented framework that constructs a Circuit KG (ChipKG) from specifications and uses adaptive, CSA-guided retrieval to enable iterative, verifiable reasoning. Evaluated on SpecEval-QA, ChipMind achieves state-of-the-art performance with an average improvement of 34.59% and up to 72.73% over strong baselines, and introduces Atomic-ROUGE as a factuality-focused evaluation metric with high correlation to human judgments. The approach demonstrates a practical path to deploying LLM-aided hardware design in industry by improving context coverage, reasoning reliability, and evaluation rigor.

Abstract

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).

Paper Structure

This paper contains 33 sections, 5 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs in Chip Design and Verification
  4. Retrieval-Augmented Generation (RAG)
  5. Reasoning-Augmented Frameworks
  6. Methodology
  7. ChipMind Framework Overview
  8. Circuit Semantic-Aware Knowledge Graph
  9. Circuit Semantic Anchoring and Categorization.
  10. Hierarchical Triple Extraction.
  11. ChipKG-Augmented Reasoning
  12. Information-Theoretic Adaptive Top-K Retrieval.
  13. CSA-based Semantic Filtering.
  14. ChipKG-Augmented Reasoning Workflow.
  15. Atomic-ROUGE
  16. ...and 18 more sections

Figures (6)

  • Figure 1: Probability density of specification tokens and design code lines in public LAD benchmarks
  • Figure 2: Overview of the ChipMind Framework and an Example of ChipKG Construction
  • Figure 3: Performance Gains by Task Type
  • Figure 4: Retrieval Distribution at Top-K. Top: Half-violin plots illustrating probability density. Bottom: Box-and-whisker plots summarizing distribution characteristics.
  • Figure 5: Results of Ablation Experiments
  • ...and 1 more figures