{"title":"Expanding SafeSU capabilities by leveraging security frameworks for contention monitoring in complex SoCs","authors":"","doi":"10.1016/j.future.2024.107518","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p><p>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.</p><p>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.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004825/pdfft?md5=297490284a18935898d8133344acc50d&pid=1-s2.0-S0167739X24004825-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24004825","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.