标题： TD-解释器：通过视觉语言学习增强对时序图的理解 显示英文标题

标题： TD-Interpreter: Enhancing the Understanding of Timing Diagrams with Visual-Language Learning

摘要： 我们引入了TD-Interpreter，这是一种专门的机器学习工具，帮助工程师在设计和验证过程中理解来自第三方的复杂时序图（TDs）。 TD-Interpreter是一个可视化问答环境，允许工程师输入一组TDs并针对这些TDs提出设计和验证问题。 我们通过微调LLaVA，一个轻量级的7B多模态大语言模型（MLLM），实现了TD-Interpreter。 为了解决训练数据可用性有限的问题，我们开发了一个合成数据生成工作流程，将视觉信息与其文本解释对齐。 我们的实验评估证明了TD-Interpreter的实用性，在评估的基准测试中，其表现远远优于未调整的GPT-4o。 显示英文摘要

摘要： We introduce TD-Interpreter, a specialized ML tool that assists engineers in understanding complex timing diagrams (TDs), originating from a third party, during their design and verification process. TD-Interpreter is a visual question-answer environment which allows engineers to input a set of TDs and ask design and verification queries regarding these TDs. We implemented TD-Interpreter with multimodal learning by fine-tuning LLaVA, a lightweight 7B Multimodal Large Language Model (MLLM). To address limited training data availability, we developed a synthetic data generation workflow that aligns visual information with its textual interpretation. Our experimental evaluation demonstrates the usefulness of TD-Interpreter which outperformed untuned GPT-4o by a large margin on the evaluated benchmarks.