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Reliability is necessary in safety-critical applications spanning numerous domains. Conventional hardware-based fault tolerance techniques, such as component redundancy, ensure reliability, typically at the expense of significantly increased power consumption, and almost double (or more) hardware area. To mitigate these costs, microarchitectural fault tolerance methods try to lower overheads by leveraging microarchitectural insights, but prior evaluations focus primarily on only application performance. As different safety-critical applications prioritize different requirements beyond reliability, evaluating only limited metrics cannot guarantee that microarchitectural methods are practical and usable for all different application scenarios. To this end, in this work, we extensively characterize and compare three fault detection methods, each representing a different major fault detection category, considering real requirements from diverse application settings and employing various important metrics such as design area, power, performance overheads and latency in detection. Through this analysis, we provide important insights which may guide designers in applying the most effective fault tolerance method tailored to specific needs, advancing the overall understanding and development of robust computing systems. For this, we study three methods for hardware error detection within a processor, i.e., (i) Dual Modular Redundancy (DMR) as a conventional method, and (ii) Redundant Multithreading (R-SMT) and (iii) Parallel Error Detection (ParDet) as microarchitecture-level methods. We demonstrate that microarchitectural fault tolerance, i.e., R-SMT and ParDet, is comparably robust compared to conventional approaches (DMR), however, still exhibits unappealing trade-offs for specific real-world use cases, thus precluding their usage in certain application scenarios. |
