Sustainability-Oriented Evaluation and Optimization for MPSoC Task Allocation and Scheduling Under Thermal and Energy Variations
Sustainability-Oriented Evaluation and Optimization for MPSoC Task Allocation and Scheduling Under Thermal and Energy Variations
复制标题
热和能量变化下 MPSoC 任务分配和调度的面向可持续性的评估和优化
DOI：
10.1109/tsusc.2017.2723500
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发表时间：
--
影响因子：
3.9
通讯作者：
Qi Zhu
中科院分区：
文献类型：
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作者：
Mingsong Chen;Xinqian Zhang;Haifeng Gu;Tongquan Wei;Qi Zhu
为了追求高性能，越来越多的网络物理系统(CPSS)采用多处理器片上系统(MPSoC)作为计算单元。然而，由于芯片上晶体管的集成度越来越高，MPSoC芯片的功率密度以及性能变化都在急剧增加。因此，基于MPSoC的CPSS可能变得不可持续和不可靠。虽然已经提出了各种任务分配和调度(TAS)启发式算法来最小化MPSoC设计的热点时间(即热紧急事件持续时间)和能耗，但很少有启发式算法能够在不违反能量、热和时间约束的情况下保证在工艺变化的情况下获得最高的性能产出。为了应对这些挑战，本文提出了一种新的基于能量和热量的TAS评估和优化框架。该方法基于统计模型检验技术，能够对能量和热联合约束下的实时MPSoC设计的性能成品率进行准确的建模和推理。为了实现系统级的设计空间探索，我们提出了一种基于回归分析的方法，它可以极大地减少总体探索的工作量。实验结果表明，我们的全自动化方法不仅可以在指定的热和能量约束下对TAS解决方案进行准确的面向可持续发展的推理，而且可以在不同的MPSoC架构上以最高的性能产出快速搜索最优的TAS解决方案。
Aiming at high performance, more and more Cyber-Physical Systems (CPSs) adopt Multiprocessor System-on-Chips (MPSoCs) as computation units. However, due to increasing integration of transistors on a die, the power densities together with performance variations of MPSoC chips have been increasing dramatically. Consequently, the MPSoC-based CPSs might become unsustainable and unreliable. Although various Task Allocation and Scheduling (TAS) heuristics have been proposed to minimize the hotspot time (i.e., duration of thermal emergency) and energy consumption of MPSoC designs, few of them can guarantee the highest performance yield under process variations without violating energy, thermal and timing constraints. To address these challenges, this paper proposes a novel energy- and thermal-aware TAS evaluation and optimization framework. Based on statistical model checking techniques, our approach enables accurate modeling and reasoning of the performance yield of real-time MPSoC designs under joint energy and thermal constraints. To enable system-level design space exploration, we propose a regression analysis-based method that can drastically reduce the overall exploration efforts. Experimental results show that our fully-automated approach can not only allow accurate sustainability-oriented reasoning of TAS solutions under specified thermal and energy constraints, but also enable the quick search of optimal TAS solutions on different MPSoC architectures with the highest performance yield.