• DocumentCode
    3691867
  • Title

    Towards Automatic Code Generation of Run-Time Power Management for Embedded Systems Using Formal Methods

  • Author

    Asieh Salehi Fathabadi;Luis Alfonso Maeda-Nunez;Michael J. Butler;Bashir M. Al-Hashimi;Geoff V. Merrett

  • Author_Institution
    Electron. &
  • fYear
    2015
  • Firstpage
    104
  • Lastpage
    111
  • Abstract
    Run-Time Management (RTM) systems are used to control energy hooks at run-time to minimise the energy consumption of embedded systems with single and many-core processors. Typically, such RTM systems are aware of application requirements and utilise workload prediction and machine learning algorithms to estimate the optimal configuration. An RTM mechanism should not compromise the reliability or performance of the platform it is managing. Because of the potential complexity and interaction with the platform and its applications, we are using rigorous design methods that allow us to master the complexity and verify the correctness of our designs in a formal way. The formal RTM design can be verified earlier in the development process before implementation, which early verification can reduce the cost of fixing potential failures which can be very demanding in testing the system after implementation. In addition, the formal model of a RTM system can be automatically translated into executable code to be executed on the hardware. Automatic code generation reduces the efforts of hand-coded implementation and is portable across different architectures and Operating Systems (OSs). In this paper we propose a formal approach toward automatic generation of RTM system code, for a video decoder application, from a verified formal model of a RTM. The formal model of the RTM system is developed using the Event-B formal modelling language and is verified using theorem proving and model checking. The automatically generated RTM system has been integrated in an embedded platform as a Linux governor, and provides up to 4% improvement over Linux´s default Ondemand governor.
  • Keywords
    "Prediction algorithms","Mathematical model","Hardware","Decoding","Monitoring","Algorithm design and analysis","Learning (artificial intelligence)"
  • Publisher
    ieee
  • Conference_Titel
    Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 2015 IEEE 9th International Symposium on
  • Type

    conf

  • DOI
    10.1109/MCSoC.2015.28
  • Filename
    7328193