Reliability is a major concern for nanoscale CMOS circuits. Degradation phenomena such as Electromigration, Negative Bias Temperature Instability, Time Dependent Dielectric Breakdown worsen with transistor scaling. Dynamic Reliability Management (DRM) techniques reduce reliability loss at runtime by constraining operating points, but they face the challenge of reducing user experience degradation while meeting a lifetime target. In this work we propose a sensor based hierarchical controller for multicore processor DRM, exploiting the major gap between the time scales of workload variations and reliability loss. We improve performance and user experience by locally relaxing reliability-induced operating point constraints, while meeting them over the large time windows relevant for reliability. With respect to the state-of-the-art, our solution guarantees timely execution of 100% of latency-critical applications, and have a 4% performance improvement over the whole lifetime.

Reliability is a major concern in multiprocessors. Dynamic Reliability Management (DRM) aims at trading off processor performance with lifetime. The state-of-the-art publications study only the theory supported by simulation. This paper presents the first complete software implementation, working on a real hardware, of a low-overhead, Android-compatible workload-aware DRM Governor for mobile multiprocessors. We discuss the design challenges and the run-time overhead involved. We show the effectiveness of our governor in guaranteeing the predefined target lifetime and show that it achieves up to 100% of lifetime improvement with respect to traditional governors, while providing comparable performance for critical applications.

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Excessive power consumption and high temperatures are some of the key factors limiting battery charge life and performance of current mobile devices or smartphones. Existing power management solutions do not account for user-experience, which can impact user satisfaction. In this paper we propose a joint power and thermal management solution, which takes a proactive approach in reducing energy consumption while providing expected user-experience. The proposed technique modulates the operating conditions based on users application preferences and exploits the "change blindness" effect to reduce display power consumption. Another important aspect of our implementation is that it does not require any restructuring of the underlying operating system. A novel thermal model of the entire smartphone was built with the purpose of monitoring and controlling the operating conditions to keep the device temperatures within safe operating ranges. Our ready-to-use management technique has been implemented on Google Nexus 5 and has been demonstrated to achieve a 46% application-specific savings on power consumption and up to 35% savings in power consumption at the device level. The mean temperature estimation error is 1.17 • C.