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Title	Edgeld: Locally Distributed Deep Learning Inference On Edge Device Clusters
ID_Doc	21859
Authors	Xue F.; Fang W.; Xu W.; Wang Q.; Ma X.; Ding Y.
Year	2020
Published	Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020
DOI	http://dx.doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00078
Abstract	Deep Neural Networks (DNN) have been widely used in a large number of application scenarios. However, DNN models are generally both computation-intensive and memory-intensive, thus difficult to be deployed on resource-constrained edge devices. Most previous studies focus on local model compression or remote cloud offloading, but overlook the potential benefits brought by distributed DNN execution on multiple edge devices. In this paper, we propose EdgeLD, a new framework for locally distributed execution of DNN-based inference tasks on a cluster of edge devices. In EdgeLD, DNN models' time cost will be firstly profiled in terms of computing capability and network bandwidth. Guided by profiling, an efficient model partition scheme is designed in EdgeLD to balance the assigned workload and the inference runtime among different edge devices. We also propose to employ layer fusion to reduce communication overheads on exchanging intermediate data among devices. Experiment results show that our proposed partition scheme saves up to 15.82% of inference time with regard to the conventional solution. Besides, applying layer fusion can speedup the DNN inference by 1.11-1.13X. When combined, EdgeLD can accelerate the original inference time by 1.77-3.57X on a cluster of 2-4 edge devices. © 2020 IEEE.
Author Keywords	deep learning; deep neural network; distributed inference; edge computing

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