To avoid catastrophic events like unrecoverable system failures on mobile and embedded systems caused by soft-errors, software-based error detection and compensation techniques have been proposed. Methods like error-correction codes or redundant execution can offer high flexibility and allow for application-specific fault-tolerance selection without the needs of special hardware supports. However, such software-based approaches may lead to system overload due to the execution time overhead. An adaptive deployment of such techniques to meet both application requirements and system constraints is desired. From our case study, we observe that a control task can tolerate limited errors with acceptable performance loss. Such tolerance can be modeled as a (m,k) constraint which requires at least m correct runs out of any k consecutive runs to be correct. In this paper, we discuss how a given (m,k) constraint can be satisfied by adopting patterns of task instances with individual error detection and compensation capabilities. We introduce static strategies and provide a formal feasibility analysis for validation. Furthermore, we develop an adaptive scheme that extends our initial approach with online awareness that increases efficiency while preserving analysis results. The effectiveness of our method is shown in a real-world case study as well as for synthesized task sets.
In this paper, we present a novel system modeling language which targets primarily the development of source-level multiprocessor memory aware optimizations. In contrast to previous system modeling approaches this approach tries to model the whole system and especially the memory hierarchy in a structural and semantically accessible way. Previous approaches primarily support generation of simulators or retargetable code selectors and thus concentrate on pure behavioral models or describe only the processor instruction set in a semantically accessible way, A simple, database-like, interface is offered to the optimization developer, which in conjunction with the MACCv2 framework enables rapid development of source-level architecture independent optimizations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.