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Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
Open accessAbstract
As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, however, by no means trivial, since it incurs high performance and energy overhead. While offloading DNNs to the cloud for execution suffers unpredictable performance, due to the uncontrolled long wide-area network latency. To address these challenges, in this paper, we propose Edgent, a collaborative and on-demand DNN co-inference framework with device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that adaptively partitions DNN computation between device and edge, in order to leverage hybrid computation resources in proximity for real-time DNN inference. (2) DNN right-sizing that accelerates DNN inference through early-exit at a proper intermediate DNN layer to further reduce the computation latency. The prototype implementation and extensive evaluations based on Raspberry Pi demonstrate Edgent's effectiveness in enabling on-demand low-latency edge intelligence.
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In the citing paper
“Cachier can increase responsiveness by 3× or more.”
From Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019) · cites this paper as [93]
Experimental — accuracy not yet measuredIn the citing paper
“a recent new trend is to optimize wireless communication by Artificial Intelligence (AI) techniques [4] [5], include but not limited to the application of AI for Physical Layer (PHY), Data Link Layer, and traffic control [6].”
From In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2019) · cites this paper as [4]
In the citing paper
“3) Temporal Isolation: most of them do not consider the longterm effect of current decision on resource allocation except for Lyapunov optimization, viz., in a highly time-varying MEC system, most of proposed optimization algorithms is optimal or close-to-optimal only for a snapshot of the system.”
From In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2019) · cites this paper as [4]
In the citing paper
“2) Dynamic Conditions: dynamics of the integrated communication and computation system are not well addressed;”
From In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2019) · cites this paper as [4]
In the citing paper
“1) Uncertain Inputs: they assume the that some key information factors are given as inputs, but actually some of them are difficult to obtain due to variant wireless channels and privacy policies;”
From In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2019) · cites this paper as [4]
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Metadata
- Authors
- Li, E., Zhou, Z., & Chen, X.
- Affiliations
- Sun Yat-sen University (CN)
- Venue
- Proceedings of the 2018 Workshop on Mobile Edge Communications
- Publisher
- ACM
- Published
- 2018-08-07
- Pages
- 31-36
- OpenAlex
- W2809251854
- Cited by
- 385
- References
- 13
- Citations by year
- 2026: 31 · 2025: 38 · 2024: 49 · 2023: 55 · 2022: 62 · 2021: 57 · 2020: 58 · 2019: 31 · 2018: 4
- FWCI
- 31.00
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Funders
- National Natural Science Foundation of China (U1711265)
- Program for Guangdong Introducing Innovative and Enterpreneurial Teams (2017ZT07X355)
- National Key Research and Development Program of China (2017YFB1001703)
- Fundamental Research Funds for the Central Universities (17lgjc40)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- edge AI, deep neural network, deep neural network inference, DNN partitioning, early-exit networks, mobile devices, low-latency inference, resource-constrained devices
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