Temperature-Aware Task Allocation and Scheduling for Embedded Multiprocessor Systems-on-Chip (MPSoC) Design

Xie, Yuan; Hung, Wei-Lun

doi:10.1007/s11265-006-9760-y

Cited by 94 publications

(66 citation statements)

References 21 publications

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“…These approaches reduce the effort of the cooling techniques and alleviate the reliability issues. We can find the works related to thermal-aware floorplanning [7], and the task selection and task assignment algorithms [20].…”

Section: Power Optimizationmentioning

confidence: 99%

GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation

Zapater

Ayala

Moya

2012

Ubiquitous Computing and Ambient Intelligence

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Abstract. In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

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Section: Power Optimizationmentioning

confidence: 99%

GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation

Zapater

Ayala

Moya

2012

Ubiquitous Computing and Ambient Intelligence

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“…As discussed earlier, for lifetime reliability threats, we should consider the more reasonable increasing failure rates during the task allocation and scheduling process. Many recent studies on task scheduling for MPSoC systems aimed at balancing different processors' temperatures or keeping them under a threshold (e.g., [14], [29], [30]). These techniques might improve the system's lifetime reliability implicitly, since operational temperature has a significant impact on ICs' lifetime reliability.…”

Section: Task Allocation and Scheduling For Mpsoc Designsmentioning

confidence: 99%

“…In other words, processors' failure rates are assumed to be independent of their usage history, which is obviously inaccurate: a typical wearout failure mechanism will have increasing failure rate as the circuit ages [12], [13]. Recently, some thermal-aware task scheduling techniques have been proposed in the literature (e.g., [14]). As ICs' failure rates are strongly related to their operational temperature, these techniques may implicitly improve the MPSoC's lifetime reliability by balancing different processors' temperatures or keeping them under a safe threshold.…”

Section: Introductionmentioning

confidence: 99%

On Task Allocation and Scheduling for Lifetime Extension of Platform-Based MPSoC Designs

Huang

Yuan

2011

IEEE Trans. Parallel Distrib. Syst.

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Abstract-With the relentless scaling of semiconductor technology, the lifetime reliability of today's multiprocessor system-on-a-chip (MPSoC) designs has become one of the major concerns for the industry. Without explicitly taking this issue into consideration during the task allocation and scheduling process, existing works may lead to imbalanced aging rates among processors, thus reducing the system's service life. To tackle this problem, in this paper, we propose an analytical model to estimate the lifetime reliability of multiprocessor platforms when executing periodical tasks, and we present a novel task allocation and scheduling algorithm that is able to take the aging effects of processors into account, based on the simulated annealing technique. In addition, to speed up the annealing process, several techniques are proposed to simplify the design space exploration process with satisfactory solution quality. Experimental results on various hypothetical multiprocessors and task graphs show that significant system lifetime extension can be achieved by using the proposed approach, especially for heterogeneous platforms with large task graphs.Index Terms-Lifetime reliability, aging effect, multiprocessor system-on-a-chip, task allocation and scheduling.

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“…Temperature aware allocation and scheduling in MPSoC for peak temperature reduction have been examined as well. Xie and Hung [21] present an allocation scheme where the maximum temperature is used as one of the factors to drive the allocation of tasks to cores. Chantem et al [7] formulate the problem of allocation and scheduling on an MPSoC as a mixed integer linear programming problem.…”

Section: Related Workmentioning

confidence: 99%

Temperature aware task sequencing and voltage scaling

Jayaseelan

Mitra

2008

2008 IEEE/ACM International Conference on Computer-Aided Design

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Abstract-On-chip power density and temperature are rising exponentially with decreasing feature sizes. This alarming trend calls for temperature management at every level of system design. In this paper, we propose task sequencing as a powerful and complimentary mechanism to voltage scaling in improving the thermal profile of an embedded system executing a set of periodic heterogenous tasks under timing constraints. We first derive the peak temperature of a repeating task sequence analytically and develop a heuristic to construct the task sequence that minimizes the peak temperature. Experimental evaluation shows that our task sequencing heuristic achieves peak temperature within 0.5 o C of the optimal solution and 7.47 o C lower, on an average, compared to the worst sequence for a large range of embedded task sets. We also propose an iterative algorithm that combines task sequencing with voltage scaling to further lower the peak temperature while satisfying the timing constraints. For embedded task sets, our combined task sequencing and voltage scaling approach achieves, on an average, 2.1 o C − 6.94 o C reduction in peak temperature compared to voltage scaling alone.

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Temperature-Aware Task Allocation and Scheduling for Embedded Multiprocessor Systems-on-Chip (MPSoC) Design

Cited by 94 publications

References 21 publications

GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation

GreenDisc: A HW/SW Energy Optimization Framework in Globally Distributed Computation

On Task Allocation and Scheduling for Lifetime Extension of Platform-Based MPSoC Designs

Temperature aware task sequencing and voltage scaling

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