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DAC '98 Proceedings of the 35th annual Design Automation Conference
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DAC '98 Proceedings of the 35th annual Design Automation Conference
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DAC '98 Proceedings of the 35th annual Design Automation Conference
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Voltage scheduling problem for dynamically variable voltage processors
ISLPED '98 Proceedings of the 1998 international symposium on Low power electronics and design
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Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
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SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
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Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Voltage setup problem for embedded systems with multiple voltages
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INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
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Thermal analysis of multiprocessor SoC applications by simulation and verification
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An optimal solution for the heterogeneous multiprocessor single-level voltage-setup problem
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A power-aware online scheduling algorithm for streaming applications in embedded MPSoC
PATMOS'10 Proceedings of the 20th international conference on Integrated circuit and system design: power and timing modeling, optimization and simulation
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ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
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This paper develops energy-driven completion ratio guaranteed scheduling techniques for the implementation of embedded software on multiprocessor systems with multiple supply voltages. We leverage application's performance requirements, uncertainties in execution time, and tolerance for reasonable execution failures to scale each processor's supply voltage at run-time to reduce the multiprocessor system's total energy consumption. Specifically, we study how to trade the difference between the system's highest achievable completion ratio Qmax and the required completion ratio Q0 for energy saving. First, we propose a best-effort energy minimization algorithm (BEEM1) that achieves Qmax with the provably minimum energy consumption. We then relax its unrealistic assumption on the application's real execution time and develop algorithm BEEM2 that only requires the application's best- and worst-case execution times. Finally, we propose a hybrid offline on-line completion ratio guaranteed energy minimization algorithm (QGEM) that provides the required Q0 with further energy reduction based on the probabilistic distribution of the application's execution time. We implement the proposed algorithms and verify their energy efficiency on real-life DSP applications and the TGFF random benchmark suite. BEEM1, BEEM2, and QGEM all provide the required completion ratio with average energy reduction of 28.7, 26.4, and 35.8%, respectively.