Process-variation-aware mapping of best-effort and real-time streaming applications to MPSoCs

  • Authors:

  • Davit Mirzoyan;Benny Akesson;Kees Goossens

  • Affiliations:

  • Delft University of Technology;Eindhoven University of Technology;Eindhoven University of Technology

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS) - Special Section ESFH'12, ESTIMedia'11 and Regular Papers
  • Year:
  • 2014

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Abstract

As technology scales, the impact of process variation on the maximum supported frequency (FMAX) of individual cores in a multiprocessor system-on-chip (MPSoC) becomes more pronounced. Task allocation without variation-aware performance analysis can greatly compromise performance and lead to a significant loss in yield, defined as the percentage of manufactured chips satisfying the application timing requirement. We propose variation-aware task allocation for best-effort and real-time streaming applications modeled as task graphs. Our solutions are primarily based on the throughput requirement, which is the most important timing requirement in many real-time streaming applications. The four main contributions of this work are (1) distinguishing best-effort firm real-time and soft real-time application classes, which require different optimization criteria, (2) using dataflow graphs, which are well suited for modeling and analysis of streaming applications, we explicitly model task execution both in terms of clock cycles (which is independent of variation) and seconds (which does depend on the variation of the resource), which we connect by an explicit binding, (3) we present two optimization approaches, which give different improvement results at different costs, (4) we present both exhaustive and heuristic algorithms that implement the optimization approaches. Our variation-aware mapping algorithms are tested on models of seven real applications and are compared to mapping methods that are unaware of hardware variation. Our results demonstrate (1) improvements in the average performance (3% on average) for best-effort applications, and (2) for firm real-time and soft real-time applications, yield improvements of up to 27% with an average of 15%, showing the effectiveness of our approaches.