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To protect multicores from soft-error perturbations, research has explored various resiliency schemes that provide high soft-error coverage. However, these schemes incur high performance and energy overheads. We observe that not all soft-error perturbations affect program correctness, and some soft-errors only affect program accuracy, i.e., the program completes with certain acceptable deviations from error free outcome. Thus, it is practical to improve processor efficiency by trading off resiliency overheads with program accuracy. This article proposes the idea of declarative resilience that selectively applies strong resiliency schemes for code regions that are crucial for program correctness (crucial code) and lightweight resiliency for code regions that are susceptible to program accuracy deviations as a result of soft-errors (non-crucial code). At the application level, crucial and non-crucial code is identified based on its impact on the program outcome. A cross-layer architecture enables efficient resilience along with holistic soft-error coverage. Only program accuracy is compromised in the worst-case scenario of a soft-error strike during non-crucial code execution. For a set of machine-learning and graph analytic benchmarks, declarative resilience reduces performance overhead over a state-of-the-art system that applies strong resiliency for all program code regions from ∼ 1.43× to ∼ 1.2×.

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