First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Buffer insertion under process variations for delay minimization
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Proceedings of the conference on Design, automation and test in Europe
Timing variation-aware high-level synthesis
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Variation-aware task allocation and scheduling for MPSoC
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Variability-driven module selection with joint design time optimization and post-silicon tuning
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Performance yield-driven task allocation and scheduling for MPSoCs under process variation
Proceedings of the 47th Design Automation Conference
Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI
Process-variation-aware mapping of best-effort and real-time streaming applications to MPSoCs
ACM Transactions on Embedded Computing Systems (TECS) - Special Section ESFH'12, ESTIMedia'11 and Regular Papers
Static statistical MPSoC power optimization by variation-aware task and communication scheduling
Microprocessors & Microsystems
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Design variability due to within-die and die-to-die variations has potential to significantly reduce the maximum operating frequency and effective performance of the system in future process technology generations. When multiple cores in MPSoC have different delay distributions, the problem of assigning tasks to the cores become challenging. This paper targets system level task allocation to stochastically minimize the total execution time of an application on MPSoC under process variation. In this work, we first introduce stochastically optimal task allocation problem. We provide formal theorems of the optimality of the solution in simple scenarios. We extend our theoretical work for generic cases in normal distribution. The proposed techniques enable efficient computation of task allocation using non-stochastic analysis. We apply these techniques in allocating tasks in the embedded system benchmark suites on MPSoC. We show that deterministic solution for system-level task allocation on widely used benchmark topologies and distributions (normal distribution) is almost as good as the best probabilistic solution.