VARSHA: Variation and reliability-aware application scheduling with adaptive parallelism in the dark-silicon era

With deeper technology scaling accompanied by a worsening power-wall, an increasing proportion of chip area on a chip multiprocessor (CMP) is expected to be occupied by dark-silicon. At the same time, design challenges due to process variations and soft-errors in integrated circuits are projected to become even more severe. In this work, we propose a novel framework that leverages the knowledge of variations on the chip to perform runtime application mapping and dynamic voltage scaling to optimize system performance and energy, while satisfying dark-silicon power-constraints of the chip as well as application-specific performance and reliability constraints. Our experimental results show average savings of 35%-80% in application service-times and 13%-15% in energy consumption, compared to the state-of-the-art.