BRIDGES: Bridging Graph Modality and Large Language Models within EDA Tasks

• URL: http://arxiv.org/abs/2504.05180v1
• Date: Mon, 07 Apr 2025 15:27:32 GMT
• Title: BRIDGES: Bridging Graph Modality and Large Language Models within EDA Tasks
• Authors: Wei Li, Yang Zou, Christopher Ellis, Ruben Purdy, Shawn Blanton, José M. F. Moura,
• Abstract summary: LLM performance suffers when graphs are represented as sequential text.<n>We introduce BRIDGES, a framework designed to incorporate graph modality into LLMs for EDA tasks.<n>Results demonstrate 2x to 10x improvements across multiple tasks compared to text-only baselines.
• Score: 12.683482535955314
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While many EDA tasks already involve graph-based data, existing LLMs in EDA primarily either represent graphs as sequential text, or simply ignore graph-structured data that might be beneficial like dataflow graphs of RTL code. Recent studies have found that LLM performance suffers when graphs are represented as sequential text, and using additional graph information significantly boosts performance. To address these challenges, we introduce BRIDGES, a framework designed to incorporate graph modality into LLMs for EDA tasks. BRIDGES integrates an automated data generation workflow, a solution that combines graph modality with LLM, and a comprehensive evaluation suite. First, we establish an LLM-driven workflow to generate RTL and netlist-level data, converting them into dataflow and netlist graphs with function descriptions. This workflow yields a large-scale dataset comprising over 500,000 graph instances and more than 1.5 billion tokens. Second, we propose a lightweight cross-modal projector that encodes graph representations into text-compatible prompts, enabling LLMs to effectively utilize graph data without architectural modifications. Experimental results demonstrate 2x to 10x improvements across multiple tasks compared to text-only baselines, including accuracy in design retrieval, type prediction and perplexity in function description, with negligible computational overhead (<1% model weights increase and <30% additional runtime overhead). Even without additional LLM finetuning, our results outperform text-only by a large margin. We plan to release BRIDGES, including the dataset, models, and training flow.

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