@article{eprints3573,
            year = {2016},
           title = {Workload Change Point Detection for Runtime Thermal Management of Embedded Systems},
          number = {8},
           pages = {1358--1371},
          volume = {35},
          author = {Anup Das and Geoff V. Merrett and Mirco Tribastone and Bashir M. Al-Hashimi},
       publisher = {IEEE},
            note = {SCOPUS ID: 2-s2.0-84979539453; WOS Accession Number: WOS:000380061700011},
         journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
        abstract = {Applications executed on multicore embedded systems interact with system software [such as the operating system (OS)] and hardware, leading to widely varying thermal profiles which accelerate some aging mechanisms, reducing the lifetime reliability. Effectively managing the temperature therefore requires: 1) autonomous detection of changes in application workload and 2) appropriate selection of control levers to manage thermal profiles of these workloads. In this paper, we propose a technique for workload change detection using density ratio-based statistical divergence between overlapping sliding windows of CPU performance statistics. This is integrated in a runtime approach for thermal management, which uses reinforcement learning to select workload-specific thermal control levers by sampling on-board thermal sensors. Identified control levers override the OSs native thread allocation decision and scale hardware voltage-frequency to improve average temperature, peak temperature, and thermal cycling. The proposed approach is validated through its implementation as a hierarchical runtime manager for Linux, with heuristic-based thread affinity selected from the upper hierarchy to reduce thermal cycling and learningbased voltage-frequency selected from the lower hierarchy to reduce average and peak temperatures. Experiments conducted with mobile, embedded, and high performance applications on ARM-based embedded systems demonstrate that the proposed approach increases workload change detection accuracy by an average 3.4{$\times$}, reducing the average temperature by 4 ?C-25 ?C, peak temperature by 6 ?C-24 ?C, and thermal cycling by 7\%-35\% over state-of-the-art approaches.},
             url = {http://eprints.imtlucca.it/3573/},
        keywords = {Embedded systems, Hardware, Thermal management, Temperature dependence, Central Processing Unit, Voltage control, Thermal stresses}
}