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Software modification aided transient error tolerance for embedded systems

机译:软件修改辅助嵌入式系统的瞬态容错

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摘要

Commercial off-the-shelf (COTS) components are increasingly being employed in embedded systems due to their high performance at low cost. With emerging reliability requirements, design of these components using traditional hardware redundancy incur large overheads, time-demanding re-design and validation. To reduce the design time with shorter time-to-market requirements, software-only reliable design techniques can provide with an effective and low-cost alternative. This paper presents a novel, architecture-independent software modification tool, SMART (Software Modification Aided transient eRror Tolerance) for effective error detection and tolerance. To detect transient errors in processor datapath, control flow and memory at reasonable system overheads, the tool incorporates selective and non-intrusive data duplication and dynamic signature comparison. Also, to mitigate the impact of the detected errors, it facilitates further software modification implementing software-based check-pointing. Due to automatic software based source-to-source modification tailored to a given reliability requirement, the tool requires no re-design effort, hardware- or compiler-level intervention. We evaluate the effectiveness of the tool using a XentiumR processor based system as a case study of COTS based systems. Using various benchmark applications with single-event upset (SEUs) based error model, we show that up to 91% of the errors can be detected or masked with reasonable performance, energy and memory footprint overheads.
机译:商用现货(COTS)组件由于其高性能和低成本而越来越多地用于嵌入式系统中。随着对可靠性的要求不断提高,使用传统硬件冗余进行这些组件的设计会产生大量开销,需要进行重新设计和验证。为了缩短上市时间,缩短设计时间,纯软件可靠的设计技术可以提供有效且低成本的选择。本文介绍了一种新颖的,与体系结构无关的软件修改工具SMART(软件修改辅助瞬态误差容限),用于有效的错误检测和容错。为了在合理的系统开销下检测处理器数据路径中的瞬时错误,控制流和内存,该工具结合了选择性和非侵入式数据复制以及动态签名比较。同样,为了减轻检测到的错误的影响,它有助于进一步的软件修改,实现基于软件的检查点。由于针对给定的可靠性要求量身定制的基于软件的自动源到源修改,该工具不需要重新设计,硬件或编译器级别的干预。我们使用基于XentiumR处理器的系统作为基于COTS的系统的案例研究来评估该工具的有效性。通过将各种基准测试应用程序与基于单事件翻转(SEU)的错误模型一起使用,我们显示出可以通过合理的性能,能耗和内存占用开销来检测或掩盖多达91%的错误。

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