A Novel Optimum Data Duplication Approach for Soft Error Detection

Soft errors are a growing concern for computer reliability. To mitigate the effects of soft errors, a variety of software-based fault tolerance methodologies have been proposed for their low costs. Data duplication techniques have the advantage of flexible and general implementation with strong capacity for error detection. However, the trade-off between reliability, performance and memory overhead should be carefully considered before employing data duplication techniques. In this paper, we first introduce an analytical model, named PRASE (Program Reliability Analysis with Soft Errors), which is able to access the impact of soft errors for the reliability of a program. Furthermore, the analytical result of PRASE points out a factor about data reliability weight, which meters the criticality of data for the overall reliability of the target program. Based on PRASE, we propose a novel data duplication approach, called ODD, which can provide the optimum error coverage under system performance constraints. To illustrate the effectiveness of our method, we perform several fault injection experiments and performance evaluations on a set of simple benchmark programs using the SimpleScalar tool set.