Self-timed Scheduling and Execution of Nonlinear Pipelines with Parallel Stages

  • Lars Lucas
  • Tobias Schuele
  • Wolfgang Schwitzer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8063)


Applications that process continuous streams of data, e.g., sensor signals, video images, network packets, etc. are well-suited for pipelined execution on multicore processors. In many cases, however, the applications are subject to real-time constraints, especially in embedded systems. Besides maximizing the throughput, it is therefore important to minimize deviations in the timing. To solve this problem, we propose a method for self-timed scheduling and parallel execution of stream-based applications in soft real-time environments. Our experimental results show significantly lower latencies compared to state-of-the-art approaches, while achieving high throughput.


Schedule Algorithm Processor Core Pareto Frontier Task Graph Dynamic Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lars Lucas
    • 1
  • Tobias Schuele
    • 2
  • Wolfgang Schwitzer
    • 1
  1. 1.Fakultät für InformatikTechnische Universität MünchenGarchingGermany
  2. 2.Corporate TechnologySiemens AGMünchenGermany

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