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  • Research Article
  • Open Access
  • Self-Timed Scheduling Analysis for Real-Time Applications

    EURASIP Journal on Advances in Signal Processing20072007:083710

    • Received: 1 September 2006
    • Accepted: 2 April 2007
    • Published:


    This paper deals with the scheduling analysis of hard real-time streaming applications. These applications are mapped onto a heterogeneous multiprocessor system-on-chip (MPSoC), where we must jointly meet the timing requirements of several jobs. Each job is independently activated and processes streams at its own rate. The dynamic starting and stopping of jobs necessitates the usage of self-timed schedules (STSs). By modeling job implementations using multirate data flow (MRDF) graph semantics, real-time analysis can be performed. Traditionally, temporal analysis of STSs for MRDF graphs only aims at evaluating the average throughput. It does not cope well with latency, and it does not take into account the temporal behavior during the initial transient phase. In this paper, we establish an important property of STSs: the initiation times of actors in an STS are bounded by the initiation times of the same actors in any static periodic schedule of the same job; based on this property, we show how to guarantee strictly periodic behavior of a task within a self-timed implementation; then, we provide useful bounds on maximum latency for jobs with periodic, sporadic, and bursty sources, as well as a technique to check latency requirements. We present two case studies that exemplify the application of these techniques: a simplified channel equalizer and a wireless LAN receiver.


    • Temporal Behavior
    • Temporal Analysis
    • Initiation Time
    • Average Throughput
    • Maximum Latency

    Authors’ Affiliations

    NXP Semiconductors Research, Eindhoven, 5656, AE, The Netherlands


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    © O.M. Moreira and M.J.G. Bekooij 2007

    This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.