The DOI matches its Crossref record; 12 of the 13 references it cites were found in a registry or an index.
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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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
BranchyNet: Fast inference via early exiting from deep neural networks
Teerapittayanon, S., McDanel, B., & Kung, H. T. (2016) · 2016 23rd International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.2016.7900006 · Cited by 1,326
Quantized Convolutional Neural Networks for Mobile Devices
Wu, J., Leng, C., Wang, Y., Hu, Q., & Cheng, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.521 · Cited by 1,273
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile ApplicationsPreprint
Kim, Y., Park, E., Yoo, S., Choi, T., Lu, Y., & Shin, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06530 · Cited by 736
Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3037697.3037698 · Cited by 673
A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering
Wang, D., & Nyberg, E. (2015) · doi:10.3115/v1/p15-2116 · Cited by 358
Research works printed but not linked: 1
- S. Han , H. Mao , and W. Dally . 2015 . Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. Fiber (2015). S. Han, H. Mao, and W. Dally. 2015. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. Fiber (2015).
Cited by
Sorted by citations
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2918951 · Cited by 2,455
“Indeed, the marriage of edge computing and AI has given rise to a new research area, namely, “edge intelligence (EI)” or “edge AI” [9], [10].”
BackgroundMethodSelf-citationConvergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498
“co-inference framework [162] for adaptive DNN partitioning and EEoI.”
Self-citationAll one needs to know about fog computing and related edge computing paradigms: A complete survey
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.009 · Cited by 1,368
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Deng, S., Zhao, H., Fang, W. J., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2984887 · Cited by 1,280
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800286 · Cited by 1,030
“Besides, our work [14] aims to meet the challenge of running DL on resource-constrained mobile devices.”
BackgroundMethodSelf-citation
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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