Multiendpoint DAG-Driven Joint Partitioning-Offloading and Scheduling Optimization for DNN Inference

Model partitioning techniques, which decompose and collaboratively execute subtasks of deep neural networks (DNNs), have emerged as a critical strategy for enhancing distributed inference efficiency. However, in mobile edge computing (MEC), dynamic load fluctuations at edge nodes and the complexity...

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Vydané v:	IEEE internet of things journal Ročník 12; číslo 19; s. 41087 - 41102
Hlavní autori:	Yan, Xiukun, Zhang, Xuexue, Zeng, Kai, Bai, Fenhua, Shen, Tao, Cao, Bin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive algorithms Artificial neural networks Chemical partition Collaboration Computational modeling Constrained scheduling deep neural network (DNN) inference Delays Design optimization distributed computing Dynamic loads Dynamic scheduling Edge computing Inference Internet of Things Load fluctuation Mobile computing model partitioning multiendpoint directed acyclic graph (DAG) Optimization Partitioning Pipelines Queueing Resource management Retrieval Scheduling Servers
ISSN:	2327-4662, 2327-4662
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