利用安全框架监控复杂 SoC 中的争用情况,扩展 SafeSU 功能

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-09-11 DOI:10.1016/j.future.2024.107518
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引用次数: 0

摘要

在安全关键型系统上运行的应用程序对性能的要求越来越高,这导致了使用包含多个 CPU、GPU 和人工智能加速器的复杂平台。然而,更高的平台和系统复杂性给性能验证和确认带来了挑战,因为任务间的时序干扰会以不明显的方式发生,因此无法在设计阶段明智地优化应用整合,也无法在测试阶段验证任务间的相互干扰是否在范围内。然而,现代混合关键性系统非常复杂,具有多层互连、共享缓存和硬件加速器。为此,本文提出了一种非侵入式附加方法,利用现有的安全框架和 SafeSU 基础设施,监控多层异构系统中的任务间干扰。结果表明,我们的方法可以安全地跟踪争用情况,并在不同干扰源之间适当地分解争用周期,从而为优化和验证过程提供指导。
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Expanding SafeSU capabilities by leveraging security frameworks for contention monitoring in complex SoCs

The increased performance requirements of applications running on safety-critical systems have led to the use of complex platforms with several CPUs, GPUs, and AI accelerators. However, higher platform and system complexity challenge performance verification and validation since timing interference across tasks occurs in unobvious ways, hence defeating attempts to optimize application consolidation informedly during design phases and validating that mutual interference across tasks is within bounds during test phases.

In that respect, the SafeSU has been proposed to extend inter-task interference monitoring capabilities in simple systems. However, modern mixed-criticality systems are complex, with multilayered interconnects, shared caches, and hardware accelerators. To that end, this paper proposes a non-intrusive add-on approach for monitoring interference across tasks in multilayer heterogeneous systems implemented by leveraging existing security frameworks and the SafeSU infrastructure.

The feasibility of the proposed approach has been validated in an RTL RISC-V-based multicore SoC with support for AI hardware acceleration. Our results show that our approach can safely track contention and properly break down contention cycles across the different sources of interference, hence guiding optimization and validation processes.

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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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