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Variation-Aware Reliable Many-Core System Design by Exploiting Inherent Core Redundancy

机译:通过利用固有的内核冗余,可感知变化的可靠多核系统设计

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Reliability issues are more severe in multi/many-core systems because of the integration of more devices in advanced technology nodes. To achieve robust computing in nanoscale designs, many circuit-level and architecture-level redundancy techniques had been proposed, which pose large fixed silicon area overhead and a lack of flexibility. In recent years, some methods have exploited the “inherent core redundancy” of many-core systems to implicitly implement N-modular redundant (NMR) subsystems to achieve area-efficient fault-tolerant computing. However, while facing the different levels of soft error rate, task vulnerability, and task significance in the many-core system, existing core-level redundancy methods become ineffective. To achieve robust computation in many-core systems with intercore variations and mixed workloads, we propose a variation-aware core-level redundancy scheme. Two novel approaches are presented in this scheme: 1) we construct NMR tables that store the degree of redundancy using mathematical models for systems affected by these variations and 2) we dynamically allocate each replicated task to a proper core with variation-aware mapping algorithms to achieve high reliability. Based on a modified multicore simulator, Sniper-Transient Error Process Variation (TEVR), the experimental results show that the proposed scheme can increase the reliability by 47.92% and achieve the energy saving of 39% compared with conventional core-level redundancy methods.
机译:由于在高级技术节点中集成了更多设备,因此在多核/多核系统中,可靠性问题更加严重。为了在纳米级设计中实现鲁棒的计算,已经提出了许多电路级和体系结构级的冗余技术,这些技术构成了较大的固定硅面积开销,并且缺乏灵活性。近年来,一些方法已利用多核系统的“固有核冗余”来隐式实现N-模块化冗余(NMR)子系统,以实现面积高效的容错计算。但是,尽管在多核系统中面临着不同级别的软错误率,任务脆弱性和任务重要性,但现有的内核级冗余方法却变得无效。为了在具有内核间差异和混合工作负载的多核系统中实现强大的计算,我们提出了一种可感知差异的内核级冗余方案。此方案中提出了两种新颖的方法:1)我们使用数学模型针对受这些变化影响的系统构建存储冗余度的NMR表,以及2)我们使用感知变化的映射算法将每个复制的任务动态分配给适当的核心,以实现高可靠性。实验结果表明,该方案基于改进的多核仿真器Sniper-Transient Error Process Variation(TEVR),与传统的核心级冗余方法相比,其可靠性提高了47.92%,节能39%。

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