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

2025-12-04

AI Summary

Large language models (LLMs) face significant challenges in integrated circuit (IC) design automation due to limited context length, hindering effective modeling of long-range semantics and multi-hop logical dependencies. To address this, we propose ChipMind—a novel framework centered on ChipKG, a domain-specific circuit knowledge graph. ChipMind integrates information-entropy-driven adaptive retrieval with intent-aware semantic filtering to enable knowledge-graph-enhanced multi-hop reasoning and dynamic logical dependency tracking. This approach achieves an optimal trade-off between precision and recall while overcoming fundamental bottlenecks inherent in conventional long-context processing methods. Evaluated on industrial-scale benchmarks, ChipMind delivers an average performance improvement of 34.59% (up to 72.73%) over state-of-the-art approaches, demonstrating substantial gains in accuracy, robustness, and scalability. The framework significantly advances the practical deployment of LLMs in hardware design automation.

Technology Category

• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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).

Problem

• Addresses LLMs' limited context windows for IC design
• Enhances semantic modeling and reasoning over long specifications
• Bridges gap between academic research and industrial LAD deployment

Innovation

• Knowledge graph construction for circuit specifications
• Adaptive retrieval with semantic filtering for reasoning
• Framework bridging academic research and industrial deployment