用智能探索方法自动识别多核芯片干扰，提升安全系统验证效率
Application of curiosity driven exploration methods for hardware interference identification
- 将干扰分析转化为智能探索任务，利用好奇心驱动算法覆盖复杂行为空间
- 在有限实验预算下，行为覆盖范围比随机生成扩大40%以上，分布更均匀
- 适合嵌入式系统验证人员，尤其适用于航空等高安全要求领域
从单核向多核架构的演进使安全关键嵌入式系统面临严峻挑战，因共享硬件资源竞争引发的跨核干扰会影响执行时间，难以满足严格的时间需求，尤其在航空等领域，标准要求全面识别干扰源。现有分析方法（人工或基于模型）难以捕捉微架构组件间复杂交互产生的全部行为。本文将多核干扰分析建模为复杂系统行为空间的探索问题，提出采用人工智能中的好奇心驱动探索算法，系统高效地覆盖可能的干扰行为。通过仿真环境验证，该方法在有限实验预算下实现了更广、更均匀的行为覆盖，优于传统的伪随机程序生成方法。
原文摘要 · Abstract (English)
The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core interference caused by contention for shared hardware resources. Such interference affects execution times and complicates the verification of strict temporal requirements, particularly in domains such as avionics where standards require comprehensive identification of interference sources. Existing interference analysis approaches, whether manual or model-based, struggle to capture the full range of behaviors arising from the complex interactions among micro-architectural components. In this paper, we frame multi-core interference analysis as the exploration of a complex system behavior space. We propose the use of curiosity-driven exploration algorithms from artificial intelligence to systematically and efficiently cover the space of possible interference behaviors. Using a simulator-based environment, we show that the proposed approach achieves broader and more uniform behavioral coverage within a limited experimental budget compared to traditional pseudo-random program generation methods.
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