Dynamic variability management in mobile multicore processors under lifetime constraints

Variability is a key issue in modern multiprocessors, resulting in performance and lifetime uncertainty, and high design margins. The margins can be reduced by exposing variability to software and then adapting at runtime. In this work we use sensors to monitor the variable operating conditions and the degradation rate. Based on the sensor data, our variability-aware OS scheduling algorithm assigns the workload to the cores and sets the power/performance tradeoffs to meet the mobile processor's lifetime constraints while adjusting to variability and improving the overall performance. We implement our algorithm in Android OS on a mobile phone and show that it achieves up to 160% performance improvement over the state-of-the-art while meeting the lifetime constraints.