| 1. |
- Ruggiero, Martino, et al.
(författare)
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A Cooperative, Accurate Solving Framework for Optimal Allocation, Scheduling and Frequency Selection on Energy-Efficient MPSoCs
- 2006
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Ingår i: Intl. Symposium on System-on-Chip SOC06,2006.
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Konferensbidrag (refereegranskat)abstract
- Most problems addressed by the software optimization flow for multi-processor systems-on-chip (MPSoCs) are NP-complete, and have been traditionally tackled by means of heuristics and highlevel approximations. Complete approaches have been effectively deployed only under unrealistic simplifying assumptions. We propose a novel methodology to formulate and solve to optimality the allocation, scheduling and discrete voltage selection problem for variable voltage/frequency MPSoCs, minimizing the system energy dissipation and the overhead for frequency switching. We integrate the optimization and validation steps to increase the accuracy of cost models and the confidence in quality of results. Two demonstrators are used to show the viability of the proposed methodology.
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| 2. |
- Ruggiero, Martino, et al.
(författare)
-
Reducing the Abstraction and Optimality Gaps in the Allocation and Scheduling for Variable Voltage/Frequency MPSoC Platforms
- 2009
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Ingår i: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. - 0278-0070 .- 1937-4151. ; 28:3, s. 378-391
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a novel approach to solve the allocation and scheduling problems for variable voltage/frequency multiprocessor systems-on-chip, which minimizes overall system energy dissipation. The optimality of derived system configurations is guaranteed, while the computation efficiency of the optimizer allows for solving problem instances that were traditionally considered beyond reach for exact solvers (optimality gap). Furthermore, this paper illustrates the development- and run-time software infrastructures that assist the user in developing applications and implementing optimizer solutions. The proposed approach guarantees a high level of power, performance, and constraint satisfaction predictability as from validation on the target platform, thus bridging the abstraction gap.
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