论文标题
微构造特征是嵌入式安全 - 关键系统中的软错误引起的故障执行标记：初步研究
Micro-Architectural features as soft-error induced fault executions markers in embedded safety-critical systems: a preliminary study
论文作者
论文摘要
辐射引起的软误差是安全关键实时嵌入式系统（SACRES）可靠性中最具挑战性的问题之一，通常使用双重模块化冗余（DMR）技术的不同口味来处理。由于所有领域中现代微处理者的复杂性，该解决方案变得无法承受。本文介绍了基于人工智能（AI）的硬件检测器的有前途的领域，以解决软错误。为了创建这样的核心并使其足够一般以便使用不同的软件应用程序，微体系属性是一个引人入胜的选择，作为候选故障检测功能。几个处理器已经通过专用的性能监控单元（PMU）跟踪这些功能。但是，有一个公开的问题可以理解它们在多大程度上足以检测出错误的执行。利用GEM5模拟真实计算系统的能力，执行故障注入实验和轮廓微体系构属性属性（即GEM5统计数据），本文介绍了有关检测软误差的潜在属性的全面分析的结果，以及可以通过这些功能训练的相关模型。
Radiation-induced soft errors are one of the most challenging issues in Safety Critical Real-Time Embedded System (SACRES) reliability, usually handled using different flavors of Double Modular Redundancy (DMR) techniques. This solution is becoming unaffordable due to the complexity of modern micro-processors in all domains. This paper addresses the promising field of using Artificial Intelligence (AI) based hardware detectors for soft errors. To create such cores and make them general enough to work with different software applications, microarchitectural attributes are a fascinating option as candidate fault detection features. Several processors already track these features through dedicated Performance Monitoring Unit (PMU). However, there is an open question to understand to what extent they are enough to detect faulty executions. Exploiting the capability of gem5 to simulate real computing systems, perform fault injection experiments and profile microarchitectural attributes (i.e., gem5 Stats), this paper presents the results of a comprehensive analysis regarding the potential attributes to detect soft error and the associated models that can be trained with these features.
