好奇心驱动探索方法在硬件干扰识别中的应用
Application of curiosity driven exploration methods for hardware interference identification
- National Institute for Research in Digital Science and Technology（国家数字科学与技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出用好奇心驱动探索算法系统覆盖多核干扰行为空间，在模拟器中比伪随机方法实现更广更均匀的覆盖，助力安全关键系统干扰识别。
AI中文摘要：
在安全关键嵌入式系统中，从单核架构向多核架构的转变带来了显著挑战，原因在于共享硬件资源竞争所导致的核间干扰。此类干扰影响执行时间，并使严格时间要求的验证复杂化，尤其是在航空电子等领域，标准要求全面识别干扰源。现有的干扰分析方法，无论是手动还是基于模型的，都难以捕捉微架构组件之间复杂交互所产生的全部行为范围。在本文中，我们将多核干扰分析构建为对复杂系统行为空间的探索。我们提出使用人工智能中的好奇心驱动探索算法，以系统且高效地覆盖可能的干扰行为空间。通过基于模拟器的环境，我们展示了所提出的方法在有限的实验预算内，相比传统的伪随机程序生成方法，能够实现更广泛且更均匀的行为覆盖。
英文摘要：
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.
