Harnessing Soft Computations for Low-Budget Fault Tolerance

A growing number of applications from various domains such as multimedia, machine learning and computer vision are inherently fault tolerant. However, for these soft workloads, not all computations are fault tolerant (e.g., A loop trip count). In this paper, we propose a compiler-based approach that takes advantage of soft computations inherent in the aforementioned class of workloads to bring down the cost of software-only transient fault detection. The technique works by identifying a small subset of critical variables that are necessary for correct macro-operation of the program. Traditional duplication and comparison are used to protect these variables. For the remaining variables and temporaries that only affect the micro-operation of the program, strategic expected value checks are inserted into the code. Intuitively, a computation-chain result near the expected value is either correct or close enough to the correct result so that it does not matter for non-critical variables. Overall, the proposed solution has, on average, only 19.5% performance overhead and reduces the number of silent data corruptions from 15% down to 7.3% and user-visible silent data corruptions from 3.4% down to 1.2% in comparison to an unmodified application. This unacceptable silent data corruption rate is even lower than a traditional full duplication scheme that has, on average, 57% overhead.