Error detection for soft computing applications

Hardware errors are on the rise with reducing chip sizes, and power constraints have necessitated the involvement of software in hardware error detection. At the same time, emerging workloads in the form of soft computing applications, (e.g., multimedia applications) can tolerate most hardware errors as long as the erroneous outputs do not deviate significantly from error-free outcomes. We term outcomes that deviate significantly from the error-free outcomes as Egregious Data Corruptions (EDCs). In this thesis, we propose a technique to place detectors for selectively detecting EDC causing errors in an application. Our technique identifies program locations for placing high coverage detectors for EDCs using static analysis and runtime profiling. We evaluate our technique on six benchmarks to measure the EDC coverage under given performance overhead bounds. Our technique achieves an average EDC coverage of 82%, under performance overheads of 10%, while detecting only 10% of the Non-EDC and benign faults. We also explore the performance-resilience tradeoff space, by studying the effect of compiler optimizations on the error resilience of soft computing applications, both with and without our technique.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat