High-speed DFG-level SEU vulnerability analysis for applying selective TMR to resource-constrained CGRA

Takashi Imagawa, Hiroshi Tsutsui, H. Ochi, Takashi Sato
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引用次数: 5

Abstract

In this paper, we investigate a method to achieve cost-effective selective triple modular redundancy (selective TMR) against single event upset (SEU). This method enables us to minimize the vulnerability of the target application circuit implemented on a resource-constrained coarse-grained reconfigurable architecture (CGRA). The key of the proposed method is the evaluation function to determine the vulnerable node in the data flow graph (DFG) of the target application. Since the influence of the fault in a node to the primary outputs depends on its fains and fanouts as well as the node itself, this paper proposes an enhanced evaluation function that reflects the operation of fanins/fanouts of a node. This paper also improves the method to derive weight vector which is used in the evaluation function, by assuming exponential distribution instead of linear distribution for the vulnerability of nodes. To derive a generic weight vector, we propose to solve a concatenated linear equations obtained from multiple sample applications, instead of averaging the weight vectors for applications. Using generalized inverse matrix to solve the equation, the proposed method takes less than ten seconds to extract a reasonable priority for selective TMR, which is extremely faster than the exhaustive exploration for the optimal solution that takes more than 15 hours. This paper also compares the contributions of the features use in the evaluation function, which would be insightful for designing reliability-aware CGRA architecture and synthesis tools.
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面向资源受限CGRA的选择性TMR高速dfg级SEU漏洞分析
本文研究了一种针对单事件干扰(SEU)实现低成本选择性三模冗余(selective TMR)的方法。该方法使我们能够最大限度地减少在资源受限的粗粒度可重构体系结构(CGRA)上实现的目标应用电路的脆弱性。该方法的关键是利用评估函数确定目标应用的数据流图(DFG)中的脆弱节点。由于节点故障对主输出的影响不仅取决于节点本身,还取决于节点的风扇和扇出,因此本文提出了一种增强的评估函数,反映节点风扇和扇出的运行情况。本文还改进了评价函数中权重向量的推导方法,将节点脆弱性的线性分布改为指数分布。为了得到一个通用的权重向量,我们建议求解从多个样本应用中获得的串联线性方程,而不是对应用的权重向量进行平均。该方法利用广义逆矩阵求解方程,在不到10秒的时间内提取出选择性TMR的合理优先级,比穷举搜索最优解需要15小时以上的时间要快得多。本文还比较了在评估函数中使用的特征的贡献,这将对设计可靠感知的CGRA体系结构和综合工具有一定的指导意义。
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