  The rising incidence of soft errors in hardware systems represents a
considerable risk to the reliability of deep learning systems and can
precipitate severe malfunctions. Although essential, soft error mitigation can
impose substantial costs on deep learning systems that are inherently demanding
in terms of computation and memory. Previous research has primarily explored
variations in vulnerability among different components of computing engines or
neural networks, aiming for selective protection to minimize protection
overhead. Our approach diverges from these studies by recognizing that the
susceptibility of deep learning tasks to soft errors is heavily
input-dependent. Notably, some inputs are simpler for deep learning models and
inherently exhibit greater tolerance to soft errors. Conversely, more complex
inputs are prone to soft error impact. Based on these insights, we introduce an
adaptive soft error protection strategy that tailors protection to the
computational demands of individual inputs. To implement this strategy, we
develop a metric for assessing the complexity of inputs and deploy a
lightweight machine learning algorithm to gauge input difficulty. Subsequently,
we employ robust protection for challenging inputs and minimal protection for
simpler ones. Our experimental evaluation across diverse datasets and deep
learning tasks reveals that our adaptive strategy reduces the soft error
protection overhead by an average of 46.9%, without compromising system
reliability.
