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2025年7月17日 23:49 星期四
首页 > 过刊浏览>2025年第36卷第6期 >2453-2476. DOI:10.13328/j.cnki.jos.007328
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智能化芯片设计程序测试研究综述
作者:
通讯作者:

陈俊洁,E-mail:junjiechen@tju.edu.cn

中图分类号:

TP311

基金项目:

国家自然科学基金(62322208); 华为高校合作项目


Survey on Testing of Intelligent Chip Design Program
Author:
  • 摘要
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  • 访问统计
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  • 参考文献 [114]
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  • 相似文献 [20]
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    摘要:

    在当今智能化的时代背景下, 芯片作为智能电子设备的核心组件, 在人工智能、物联网、5G通信等诸多领域发挥着关键作用, 保障芯片的正确性、安全性和可靠性至关重要. 在芯片的开发流程中, 开发人员首先需要利用硬件描述语言, 将芯片设计实现成软件形式(即芯片设计程序), 然后再进行物理设计并最终流片(即生产制造). 作为芯片设计制造的基础, 芯片设计程序的质量直接影响了芯片的质量. 因此, 针对芯片设计程序的测试具有重要研究意义. 早期的芯片设计程序测试方法主要依赖开发人员人工设计测试用例来测试芯片设计程序, 往往需要大量的人工成本和时间代价. 随着芯片设计程序复杂度的日益增长, 诸多基于仿真的自动化芯片设计程序测试方法被提出, 提升了芯片设计程序测试效率及有效性. 近年来, 越来越多的研究者致力于将机器学习、深度学习和大语言模型(LLM)等智能化方法应用于芯片设计程序测试领域. 调研88篇智能化芯片设计程序测试相关的学术论文, 从测试输入生成、测试预言构造及测试执行优化这3个角度对智能化芯片设计程序测试已有成果进行整理归纳, 重点梳理芯片设计程序测试方法从机器学习阶段、深度学习阶段到大语言模型阶段的演化, 探讨不同阶段方法在提高测试效率和覆盖率、降低测试成本等方面的潜力. 同时, 介绍芯片设计程序测试领域的研究数据集和工具, 并展望未来的发展方向和挑战.

    Abstract:

    In the current intelligent era, chips, serving as the core components of intelligent electronic devices, play a critical role in multiple fields such as artificial intelligence, the Internet of Things, and 5G communication. It is of great significance to ensure the correctness, security, and reliability of chips. During the chip development process, developers first need to implement the chip design into a software form (i.e., chip design programs) by using hardware description languages, and then conduct physical design and finally tape-out (i.e., production and manufacturing). As the basis of chip design and manufacturing, the quality of the chip design program directly impacts the quality of the chips. Therefore, the testing of chip design programs is of important research significance. The early testing methods for chip design programs mainly depend on the test cases manually designed by developers to test the chip design programs, often requiring a large amount of manual cost and time. With the increasing complexity of chip design programs, various simulation-based automated testing methods have been proposed, improving the efficiency and effectiveness of chip design program testing. In recent years, more and more researchers have been committed to applying intelligent methods such as machine learning, deep learning, and large language models (LLMs) to the field of chip design program testing. This study surveys 88 academic papers related to intelligent chip design program testing, and sorts and summarizes the existing achievements in intelligent chip design program testing from three perspectives: test input generation, test oracle construction, and test execution optimization. It focuses on the evolution of chip design program testing methods from the machine learning stage to the deep learning stage and then to the large language model stage, exploring the potential of different stages’ methods in improving testing efficiency and coverage, as well as reducing testing costs. Additionally, it introduces research datasets and tools in the field of chip design program testing and envisions future development directions and challenges.

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李晓鹏,闫明,樊兴宇,唐振韬,开昰雄,郝建业,袁明轩,陈俊洁.智能化芯片设计程序测试研究综述.软件学报,2025,36(6):2453-2476

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  • 收稿日期:2024-08-26
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