SLOGAN: SDC probability estimation using structured graph attention network

The trend of progressive technology scaling makes the computing system more susceptible to soft errors. The most critical issue that soft error incurs is silent data corruption (SDC) since SDC occurs silently without any warnings to users. Estimating SDC probability of a program is the first and essential step towards designing protection mechanism. Prior work suffers from prediction inaccuracy since the proposed heuristic-based models fail to describe the semantic of fault propagation. We propose a novel approach SLOGAN which transfers the prediction of SDC probability into a graph regression task. A program is represented in the form of dynamic dependence graph. To capture the rich semantic of fault propagation, we apply structured graph attention network, which includes node-level, graph-level and layer-level self-attention. With the learned attention coefficients from node-level, graph-level, and layer-level self-attention, the importance of edges, nodes, and layers to the fault propagation can be fully considered. We generate the graph embedding by weighted aggregation of the embeddings of nodes and compute the SDC probability by the regression model. The experiment shows that SLOGAN achieves higher SDC accuracy than state-of-the-art methods with a low time cost.