A temperature-aware and energy-efficient fuzzy technique to schedule tasks in heterogeneous MPSoC systems

Ekhtiyari, Zohreh; Moghaddas, Vahidreza; Beitollahi, Hakem

doi:10.1007/s11227-019-02807-8

Cited by 11 publications

(19 citation statements)

References 23 publications

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“…This comparison is performed for ten synthetic and real-life application graphs with various sizes of 6 to 85 tasks. SoCs [27]. In this method, the degree of processors during task scheduling and mapping are determined based on the FIS and and its rules that are statically perform some kind of biased voting toward the lower values among the main optimization objectives.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this method, the degree of processors during task scheduling and mapping are determined based on the FIS and and its rules that are statically perform some kind of biased voting toward the lower values among the main optimization objectives. Figure 13 shows the comparison results of our proposed FNN-based task scheduling approach to [27] in terms of the target main design challenges of MPSoCs. Here, the same as previous experiment, ten synthetic and real-life application graphs are considered and the distribution of normalized values for each considered objective for all application graphs are compared.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…There are some usual approaches to solve the structure identification problem including using different clustering methods [45,25,26,50], online clustering methods [62,63,37,49,51,12,53], and uniformly partitioning [35,27]. The proposed structure is a control unit that should cover the whole input space properly.…”

Section: Learning Methodsmentioning

confidence: 99%

“…These parameters could be various in different applications so exploring the whole design space and presenting a solution set is more effective. In [27], a task scheduler based on a fuzzy inference system is presented to optimize power consumption and temperature simultaneously. In this approach, the static and predefined fuzzy rules are employed to determine the appropriate processing core and the scheduling details are defined based on improving the system temperature and utilization.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs

Abdi¹,

Salimi-Badr²

2022

Preprint

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In this paper, an online task scheduling and mapping method based on a fuzzy neural network (FNN) learned by an evolutionary multi-objective algorithm (NSGA-II) to jointly optimize the main design challenges of heterogeneous MPSoCs is proposed. In this approach, first, the FNN parameters are trained using an NSGA-II-based optimization engine by considering the main design challenges of MPSoCs including temperature, power consumption, failure rate, and execution time on a training dataset consisting of different application graphs of various sizes. Next, the trained FNN is employed as an online task scheduler to jointly optimize the main design challenges in heterogeneous MPSoCs. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous in online scheduling procedures. The performance of the method is compared with some previous heuristic, meta-heuristic, and rule-based approaches in several experiments. Based on these experiments our proposed method outperforms the related studies in optimizing all design criteria. Its improvement over related heuristic and meta-heuristic approaches are estimated 10.58% in temperature, 9.22% in power consumption, 39.14% in failure rate, and 12.06% in execution time, averagely. Moreover, considering the interpretable nature of the FNN, the frequently fired extracted fuzzy rules of the proposed approach are demonstrated.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Learning Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs

Abdi¹,

Salimi-Badr²

2022

Preprint

View full text Add to dashboard Cite

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“…[59-61, 64, 67, 68] Refs. [62,63,65,69,70] Table 2 briefly summarizes the implementation methods of the above-mentioned FPGA-based hardware task scheduling research work. The scheduling strategies for optimizing time overhead mainly use configuration prefetching and module reuse technology to hide or avoid task reconfiguration.…”

Section: Resource Utilizationmentioning

confidence: 99%

A Survey: FPGA‐Based Dynamic Scheduling of Hardware Tasks

Zhang

Guo

et al. 2021

Chinese J of Electronics

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To meet the increasing computing needs of various application fields, Field programmable gate array (FPGA) has been widely deployed. In FPGA‐based processing, hardware tasks can be better accelerated by allocating appropriate computing resources. Therefore, FPGA‐based hardware task scheduling has become one of the mainstream research directions in academia and industry. However, the optimization objectives of existing FPGA‐based hardware task scheduling methods are relatively scattered. In this regard, this paper summarizes the research status of hardware task dynamic scheduling from the three essential elements of FPGA processing: time, resources, and power consumption. This paper analyzes, sorts out, categorizes the ideas and implementations of various scheduling methods and analyzes and evaluates optimization effects of various scheduling methods from multiple dimensions. Then, the shortcomings of the existing methods are summarized and some practical applications are introduced. Finally, the research direction of task scheduling based on FPGA is prospected and summarized.

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Scheduling analysis and correction for dependent real-time tasks upon heterogeneous multiprocessor architectures

Mrabet,

Karamti,

Mahfoudhi

2023

Computing

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A temperature-aware and energy-efficient fuzzy technique to schedule tasks in heterogeneous MPSoC systems

Cited by 11 publications

References 23 publications

A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs

A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs

A Survey: FPGA‐Based Dynamic Scheduling of Hardware Tasks

Scheduling analysis and correction for dependent real-time tasks upon heterogeneous multiprocessor architectures

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