Power-efficient embedded processing with resilience and real-time constraints
Low-power embedded processing typically relies on dynamic voltage-frequency scaling (DVFS) in order to optimize energy usage (and therefore, battery life). However, low voltage operation exacerbates the incidence of soft errors. Similarly, higher voltage operation (to meet real-time deadlines) is constrained by hard-failure rate limits. In this paper, we examine a class of embedded system applications relevant to mobile vehicles. We investigate the problem of assigning optimal voltage-frequency settings to individual segments within target workflows. The goal of this study is to understand the limits of achievable energy efficiency (performance per watt) under varying levels of system resilience constraints. To optimize for energy efficiency, we consider static optimization of voltage-frequency settings on a per-application-segment basis. We consider both linear and graph-structured workflows. In order to understand the loss in energy efficiency in the face of environmental uncertainties encountered by the mobile vehicle, we also study the effect of injecting random variations in the actual runtime of individual application segments. A dynamic re-optimization of the voltage-frequency settings is required to cope with such in-field uncertainties.