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

TL;DR

ChipMind enhances reasoning over long, complex circuit specifications by transforming them into knowledge graphs and employing adaptive retrieval and semantic filtering, significantly improving accuracy over existing methods.

Contribution

Introduces ChipMind, a knowledge graph-based reasoning framework with adaptive retrieval and semantic filtering for long IC specifications, addressing LLM context limitations.

Findings

Achieves 34.59% average improvement over baselines

Effectively balances retrieval completeness and precision

Bridges gap between research and industrial deployment

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).