Monthly Archives: April 2015

System-Level Energy Management in Many-Core Systems Utilising Distributed Speed-Power Controllers

System-Level Energy Management in Many-Core Systems Utilising Distributed Speed-Power Controllers

  • Anca Molnos. System-Level Energy Management in Many-Core Systems Utilising Distributed Speed-Power Controllers. Invited Lecture at COOLCHIPS 2015 (XVIIIth IEEE Symposium on Low-Power and High-Speed Chips), 2015.
    [BibTeX] [Abstract]

    Energy efficiency is one of the crucial concerns today in computing systems ranging from small connected devices to large data-centers. This issue is addressed a various levels, and recently we have witnessed a lot of progress in methods to control speed and power consumption of digital circuits. Notable examples are fine-grain adaptive voltage and frequency scaling, and the adoption of new technologies such as Fully-Depleted Silicon On Insulator (FDSOI). These advances however bring new knobs to tradeoff power and speed, e.g., supply voltage, body-bias voltage, which, in turn, open interesting questions about how to fully take advantage of their potential at software level. This talk we will present methods to reduce power consumption of applications and the tradeoffs therein. As a research vehicle, we have the case of a low-power many-core architecture with several power domains and distributed speed-power controllers. We will study the impact of adaptive voltage scaling and discuss methods to determine the optimal power modes, both with benefits at system level, in the context of advanced technologies such as FDSOI.

    @Misc{2015-04-MOLNOS,
    author = {Anca Molnos},
    title = {{System-Level Energy Management in Many-Core Systems Utilising Distributed Speed-Power Controllers}},
    howpublished = {Invited Lecture at COOLCHIPS 2015 (XVIIIth IEEE Symposium on Low-Power and High-Speed Chips)},
    date = {2015-04-13},
    address = {Yokohama, Japan},
    abstract = {Energy efficiency is one of the crucial concerns today in computing systems ranging from small connected devices to large data-centers. This issue is addressed a various levels, and recently we have witnessed a lot of progress in methods to control speed and power consumption of digital circuits. Notable examples are fine-grain adaptive voltage and frequency scaling, and the adoption of new technologies such as Fully-Depleted Silicon On Insulator (FDSOI). These advances however bring new knobs to tradeoff power and speed, e.g., supply voltage, body-bias voltage, which, in turn, open interesting questions about how to fully take advantage of their potential at software level. This talk we will present methods to reduce power consumption of applications and the tradeoffs therein. As a research vehicle, we have the case of a low-power many-core architecture with several power domains and distributed speed-power controllers. We will study the impact of adaptive voltage scaling and discuss methods to determine the optimal power modes, both with benefits at system level, in the context of advanced technologies such as FDSOI.},
    year = {2015}
    }

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Self-positioning of a team of flying smart cameras

Self-positioning of a team of flying smart cameras

  • Fabio Poiesi and Andrea Cavallaro. Self-positioning of a team of flying smart cameras. In IEEE Proceedings of Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015), pages 1-6, Singapore, 2015. doi:10.1109/ISSNIP.2015.7106943
    [BibTeX] [Abstract]

    Quadcopters are highly maneuverable and can provide an effective means for an agile dynamic positioning of sensors such as cameras. In this paper we propose a method for the self-positioning of a team of camera-equipped quadcopters (flying cameras) around a moving target. The self-positioning task is driven by the maximization of the monitored surface of the moving target based on a dynamic flight model combined with a collision avoidance algorithm. Each flying camera only knows the relative distance of neighboring flying cameras and its desired position with respect to the target. Given a team of up to 12 flying cameras, we show they can achieve a stable time-varying formation around a moving target without collisions.

    @InProceedings{2015-04-POIESI,
    title = {{Self-positioning of a team of flying smart cameras}},
    author = {Fabio Poiesi and Andrea Cavallaro},
    booktitle = {{IEEE Proceedings of Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015)}},
    address = {Singapore},
    date = {2015-04-07/2015-04-09},
    doi = {10.1109/ISSNIP.2015.7106943},
    year = {2015},
    pages = {1-6},
    abstract = {Quadcopters are highly maneuverable and can provide an effective means for an agile dynamic positioning of sensors such as cameras. In this paper we propose a method for the self-positioning of a team of camera-equipped quadcopters (flying cameras) around a moving target. The self-positioning task is driven by the maximization of the monitored surface of the moving target based on a dynamic flight model combined with a collision avoidance algorithm. Each flying camera only knows the relative distance of neighboring flying cameras and its desired position with respect to the target. Given a team of up to 12 flying cameras, we show they can achieve a stable time-varying formation around a moving target without collisions.}
    }

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