Modeling Energy Consumption for Task-Offloading Decisions on Mobile and Embedded Devices

Niemann, Christoph; Ewert, Christian; Puttnies, Henning; Rethfeldt, Michael; Timmermann, Dirk; Danielis, Peter

doi:10.1109/lifetech48969.2020.1570618809

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…A smaller γ implies less channel importance. Thus, we prune and optimize the model [11] to accelerate object detection speed and to reduce the model size for suiting embedded devices.…”

Section: Yolov4mentioning

confidence: 99%

BAFPN: An Optimization for YOLO

Li³

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Object detection is essential in Computer Vision and is widely applied in all areas. This paper proposes a method called BAFPN. BAFPN is a new bidirectional Feature Pyramid Network that constructs accurate object detection networks based on YOLOv4 by implementing Adaptively Spatial Feature Fusion. Besides, Exponential Moving Average is used to improve the network performance. The developed network not only maintains high computing speed but also enhances the mAP by 4.3% when testing with the MS COCO dataset and when comparing it to the original YOLOv4. To further improve the performance, the trained model was pruned using the Batch Normalization layer's scaling factor, achieving an 18% reduction in size and improving object detection speed.

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“…A smaller γ implies less channel importance. Thus, we prune and optimize the model [11] to accelerate object detection speed and to reduce the model size for suiting embedded devices.…”

Section: Yolov4mentioning

confidence: 99%

BAFPN: An Optimization for YOLO

Li³

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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“…Recently, some approaches have been used to study task offloading in MEC, which in order to reduce energy consumption [5] , minimize delay [6] or achieve a minimum weight of energy consumption and latency in power system [7] . Another thread of research focuses on resource allocation, applying the NOMA technology to reduce the latency by convex optimization [8] .…”

Section: Introductionmentioning

confidence: 99%

Task offloading and cross-layer resource allocation for mobile edge computing in NOMA multimode communication

Zhang,

He,

Wang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…However, as system performance improves, energy consumption is also increasing. It is estimated that by 2025, the total installed base of global connected embedded devices will even exceed 75 billion 3 . If reasonable solutions are not taken to reduce the huge power supply required by the huge number of embedded devices, it will bring severe energy shortage and environmental pollution to the world.…”

Section: Introductionmentioning

confidence: 99%

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

Deng

Shen

Cao

et al. 2021

Concurrency and Computation

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Energy consumption has become one of the main bottlenecks that limit the performance improvement of heterogeneous multiprocessor systems. In a heterogeneous distributed shared‐memory multiprocessor system (HDSMS), each processor can access all the memories, and each data can be stored in different memories. This article aims at addressing the problem of task scheduling and data allocation (TSDA) on HDSMS. To minimize the total energy consumption under a time constraint for TSDA, we propose two algorithms: the extended tree assignment for task scheduling incorporating data allocation (ETATS‐DA) and critical path task scheduling and data allocation (CPTSDA). The ETATS‐DA algorithm first utilizes the extended tree assignment to search the near optimal solution for task assignment, and then allocates data to memory based on the result of assignment. The CPTSDA algorithm considers TSDA jointly on a critical path simultaneously. Our proposed algorithms perform coherent data allocation under the consideration of best task scheduling by running two different heuristic strategies, respectively, and taking the best result as the final result. We conduct a large number of simulation experiments to test the performance of our algorithms, and the results validate the higher performance of our methods compared with the state‐of‐the‐art algorithms.

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Modeling Energy Consumption for Task-Offloading Decisions on Mobile and Embedded Devices

Cited by 5 publications

References 9 publications

BAFPN: An Optimization for YOLO

BAFPN: An Optimization for YOLO

Task offloading and cross-layer resource allocation for mobile edge computing in NOMA multimode communication

Task scheduling on heterogeneous multiprocessor systems through coherent data allocation

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