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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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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
Items 101–200 of 385
Kumar, V. D. A., Kumar, A., Batth, R. S., Rashid, M. U., Gupta, S. K., & Raghuraman, M. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4110 · Cited by 29
Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay
Mohammed, A. S., Venkatachalam, K., Hubálovský, Š., Trojovský, P., & Prabu, P. (2022) · Mobile Networks and Applications · doi:10.1007/s11036-021-01860-z · Cited by 29
Distributed Fog Computing and Federated-Learning-Enabled Secure Aggregation for IoT Devices
Liu, Y., Dong, Y., Wang, H., Jiang, H., & Xu, Q. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176305 · Cited by 30
Privacy-Aware Edge Computing Based on Adaptive DNN Partitioning
Shi, C., Chen, L., Shen, C., Song, L., & Xu, J. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013742 · Cited by 28
Liu, R., Garcia, L., Liu, Z., Ou, B., & Srivastava, M. B. (2021) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3450268.3453524 · Cited by 28
Network for Distributed Intelligence: a Survey and Future Perspectives
Campolo, C., Iera, A., & Molinaro, A. (2023) · IEEE Access · doi:10.1109/access.2023.3280411 · Cited by 33
Dong, C., Shafiq, M., Dabel, M. M. A., Sun, Y., & Tian, Z. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3339468 · Cited by 28
A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues
Sheraz, M., Chuah, T. C., Tareen, W. U. K., Al-Habashna, A., Saeed, S. I., Ahmed, M., Lee, I. E., & Guizani, M. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3568496 · Cited by 26
Lee, C.-S., Hong, S., Hong, S.-B., & Kim, T. (2020) · ETRI Journal · doi:10.4218/etrij.2020-0112 · Cited by 26
Cutting-Edge Inference: Dynamic DNN Model Partitioning and Resource Scaling for Mobile AI
Lim, J.-A., Lee, J., Kwak, J., & Kim, Y. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3466848 · Cited by 25
Hasan, T., & Tasnim, S. (2025) · Ad Hoc Networks · doi:10.1016/j.adhoc.2025.103937 · Cited by 25
Luo, P., Yu, F. R., Chen, J., Li, J., & Leung, V. C. M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3051611 · Cited by 26
Trine: Cloud-Edge-Device Cooperated Real-Time Video Analysis for Household Applications
Zhao, Y., Yang, Z., He, X., Cai, X., Miao, X., & Ma, Q. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3154721 · Cited by 28
Li, X., Xu, Z., Fang, F., Fan, Q., Wang, X., & Leung, V. C. M. (2022) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2022.3177649 · Cited by 25
Partitioning of CNN Models for Execution on Fog Devices
Dey, S., Mukherjee, A., Pal, A., & Balamuralidhar, P. (2018) · doi:10.1145/3277893.3277899 · Cited by 26
AdaKnife: Flexible DNN Offloading for Inference Acceleration on Heterogeneous Mobile Devices
Liu, S., Luo, H., Li, X., Li, Y., Guo, B., Yu, Z., Wang, Y., Ma, K., Ding, Y., & Yao, Y. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3466931 · Cited by 24
PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices
Velasco Montero, D., Fernández Berni, J., Carmona Galán, R., & Rodríguez Vázquez, Á. B. (2020) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 24
AccuMO: Accuracy-Centric Multitask Offloading in Edge-Assisted Mobile Augmented Reality
Kong, Z. J., Xu, Q., Meng, J., & Hu, Y. (2023) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3570361.3592531 · Cited by 24
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Zhang, Y., Gu, T., & Zhang, X. (2020) · 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn48710.2020.00-45 · Cited by 23
González-Sosa, E., Pérez, P., Tolosana, R., Kachach, R., & Villegas, Á. (2020) · IEEE Access · doi:10.1109/access.2020.3013016 · Cited by 23
AI Model Compression for Edge Devices Using Optimization Techniques
Kulkarni, U., Meena, S. M., Gurlahosur, S. V., Benagi, P., Kashyap, A. K., Ansari, A., & Karnam, V. E. G. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-68291-0_17 · Cited by 24
Yousefpour, A., Devic, S., Nguyen, B., Kreidieh, A., Liao, A., Bayen, A. M., & Jue, J. P. (2019) · International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT) · doi:10.1145/3363347.3363366 · Cited by 23
A Decade of Video Analytics at Edge: Training, Deployment, Orchestration, and Platforms
Bai, T., Zhao, H., Huang, L., Wang, Z., Kim, D. I., & Nallanathan, A. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3563377 · Cited by 25
Data-driven Distributionally Robust Optimization for Edge Intelligence
Zhang, Z., Lin, S., Dedeoğlu, M., Ding, K., & Zhang, J. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155440 · Cited by 22
Location-Privacy-Aware Service Migration Against Inference Attacks in Multiuser MEC Systems
Wang, W., Zhou, X., Qiu, T., He, X. Y., & Ge, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3290145 · Cited by 21
Dynamic Split Computing Framework in Distributed Serverless Edge Clouds
Ko, H., Jeong, H., Jung, D., & Pack, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3342438 · Cited by 20
DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems
Baccour, E., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322470 · Cited by 20
AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web
Huang, Y., Qiao, X., Dustdar, S., & Li, Y. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796763 · Cited by 19
Combining DNN partitioning and early exit
Ebrahimi, M., Veith, A. D. S., Gabel, M., & de Lara, E. (2022) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3517206.3526270 · Cited by 19
Edge intelligence for network intrusion prevention in IoT ecosystem
Habiba, M., Islam, M. R., Muyeen, S. M., & Ali, A. B. M. S. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108727 · Cited by 19
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks
Tian, Y., Zhang, Z., Yang, Z., & Yang, Q. (2022) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops53468.2022.9814499 · Cited by 18
FedEqual: Defending Model Poisoning Attacks in Heterogeneous Federated Learning
Chen, L.-Y., Chiu, T.-C., Pang, A., & Cheng, L.-C. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685082 · Cited by 18
DNN Partitioning for Inference Throughput Acceleration at the Edge
Feltin, T., Marchó, L., Cordero-Fuertes, J.-A., Brockners, F., & Clausen, T. (2023) · IEEE Access · doi:10.1109/access.2023.3244497 · Cited by 18
Automating Deep Neural Network Model Selection for Edge Inference
Lu, B., Yang, J., Chen, L. Y., & Ren, S. (2019) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi48466.2019.00035 · Cited by 18
NeuroMessenger: Towards Error Tolerant Distributed Machine Learning Over Edge Networks
Wang, S., & Zhang, X. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796695 · Cited by 17
Li, J., Liao, G., Chen, L., & Chen, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3312304 · Cited by 17
Self-citationEnhancement of edge-based surveillance videos based on bilateral filtering
Chen, S., Xu, J., & Wu, Q. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.01.031 · Cited by 17
Mutual match for semi-supervised online evolutive learning
Li, D., Zhu, X., & Song, L. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03564-7 · Cited by 17
An Efficient Distributed Deep Learning Framework for Fog-Based IoT Systems
Chang, Y., Huang, X., Shao, Z., & Yang, Y. (2019) · Globecom · doi:10.1109/globecom38437.2019.9014056 · Cited by 16
Zheng, Y., Zhang, T., Mu, X., Liu, Y., & Huang, R. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3568870 · Cited by 16
Posit Process Element for Using in Energy-Efficient DNN Accelerators
Zolfagharinejad, M., Kamal, M., Afzali‐Kusha, A., & Pedram, M. (2022) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2022.3165510 · Cited by 17
Real-time DNN Model Partitioning for QoE Enhancement in Mobile Vision Applications
Lim, J.-A., & Kim, Y. (2022) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon55815.2022.9918600 · Cited by 16
Cooperative Swarm Learning for Distributed Cyclic Edge Intelligent Computing
Xu, R., Jin, W., Khan, A. N., Lim, S., & Kim, D. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100783 · Cited by 17
Two-Phase Split Computing Framework in Edge–Cloud Continuum
Ko, H., Kim, B.-K., Kim, Y.-M., & Pack, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3376977 · Cited by 16
Huang, Y., Qiao, X., Ren, P., Liu, L., Pu, C., & Chen, J. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00149 · Cited by 16
Zhang, Y., Gu, T., & Zhang, X. (2020) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3384419.3430716 · Cited by 16
Human auditory model based real-time smart home acoustic event monitoring
Mondal, S., & Barman, A. D. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11455-1 · Cited by 16
Memory optimization at Edge for Distributed Convolution Neural Network
Naveen, S., & Kounte, M. R. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4648 · Cited by 17
Automated Real-Time Roadway Asset Inventory using Artificial Intelligence
Kargah-Ostadi, N., Waqar, A., & Hanif, A. (2020) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198120944926 · Cited by 15
MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Zhang, Y., Gu, T., & Zhang, X. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3103846 · Cited by 16
Research on Emotion Recognition for Online Learning in a Novel Computing Model
Chen, M., Xie, L., Li, C., & Wang, Z. (2022) · Applied Sciences · doi:10.3390/app12094236 · Cited by 15
An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic
Niu, T., Teng, Y., Han, Z., & Zou, P. (2022) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc51071.2022.9771550 · Cited by 15
Adaptive and Collaborative Edge Inference in Task Stream with Latency Constraint
Song, J., Liu, Z., Wang, X., Qiu, C., & Chen, X. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9500892 · Cited by 15
Self-citationTowards Risk-Averse Edge Computing With Deep Reinforcement Learning
Xu, D., Su, X. J., Wang, H., Tarkoma, S., & Hui, P. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3329444 · Cited by 14
Native Support of AI Applications in 6G Mobile Networks Via an Intelligent User Plane
Schwarzmann, S., Civelek, T. E., Iera, A., Corujo, D., Karetsos, G. T., Guerzoni, R., Abboud, O., Valenzuela, A. M., Trivisonno, R., Spina, M. G., et al. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570691 · Cited by 14
do Carmo, P. R. X., Bezerra, D. D. F., Filho, A. T. D. O., Freitas, E., Silva, M. L. P. C., Dantas, M., Oliveira, B., Kelner, J., Sadok, D. F. H., & Souza, R. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104024 · Cited by 14
Membership Inference Attack with Multi-Grade Service Models in Edge Intelligence
Wang, K., Hu, Z., Ai, Q., Liu, Q., Chen, M., Liu, K., & Cong, Y. (2021) · IEEE Network · doi:10.1109/mnet.011.2000246 · Cited by 14
Performance Analysis of CNN Models for Mobile Device Eye Tracking with Edge Computing
Gunawardena, N., Ginige, J. A., Javadi, B., & Lui, G. Y. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.288 · Cited by 17
Kim, H. C., Choi, J. S., Kim, J., & Ko, J. H. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.01.006 · Cited by 14
Efficient Pipeline Collaborative DNN Inference in Resource-Constrained UAV Swarm
Ren, W., Qu, Y., Qin, Z., Dong, C., Zhou, F., Zhang, L., & Wu, Q. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570535 · Cited by 13
All-Sky Autonomous Computing in UAV Swarm
Sun, H., Qu, Y., Dong, C., Dai, H., Li, Z., Zhang, L., Wu, Q., & Guo, S. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3427420 · Cited by 13
Lightweight Edge-Cloud Cooperative Inference for Small and Medium Models under WAN Constraints
Liao, J., Chang, F., Xia, T., Huang, Z., Huang, X., & Zhou, Y. (2026) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1109/eibdct69742.2026.11566691 · Cited by 12
AI Strategy for Stake Cloud Computing and Edge Computing: A State of the art survey
Aishwarya, R., & Mathivanan, G. (2021) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca52323.2021.9676013 · Cited by 12
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning
Zhang, T., Li, Z., Chen, Y., Lam, K.-Y., & Zhao, J. (2022) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00050 · Cited by 12
Liang, Y., Tan, J., Xie, Z., Chen, Z., Lin, D., & Yang, Z.-H. (2023) · Sensors · doi:10.3390/s24010240 · Cited by 12
Huang, B., Abtahi, A., & Aminifar, A. (2024) · IEEE transactions on circuits and systems for artificial intelligence. · doi:10.1109/tcasai.2024.3493036 · Cited by 15
Accelerating Mobile Edge Generation (MEG) by Constrained Learning
Xu, X., Liu, Y., Mu, X., Xing, H., & Nallanathan, A. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3558975 · Cited by 13
Condition Assessment of the Cable Trench Based on an Intelligent Inspection Robot
Jia, Z., Tian, Y., Liu, Z., & Fan, S. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.860461 · Cited by 12
Do we need sophisticated system design for edge-assisted augmented reality?
Meng, J., Kong, Z. J., Hu, Y., Choi, M. G., & Lal, D. (2022) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3517206.3526267 · Cited by 12
Parthasarathy, A., & Krishnamachari, B. (2022) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac55475.2022.9998427 · Cited by 12
Enable Pipeline Processing of DNN Co-inference Tasks In the Mobile-Edge Cloud
Hu, S., Dong, C., & Wen, W. (2021) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs52626.2021.9449178 · Cited by 12
A Survey of Cross-Layer Security for Resource-Constrained IoT Devices
Altaibek, M., Issainova, A., Aidynov, T., Kuttymbek, D., Abisheva, G., & Nurusheva, A. (2025) · Applied Sciences · doi:10.3390/app15179691 · Cited by 13
Huang, Y., Yao, T., Lin, Z., Shang, X., Yuan, Y., Cui, L., & Yang, Y. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3475590 · Cited by 11
Zhang, Y., Gu, T., & Zhang, X. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3062575 · Cited by 13
Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator
Cao, Z., Li, Z., Chen, Y., Pan, H., Hu, Y., & Liu, J. (2023) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00062 · Cited by 13
Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence.Preprint
Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2021) · arXiv (Cornell University) · Cited by 11
Coarse-to-Fine: A hierarchical DNN inference framework for edge computing
Zhang, Z., Zhang, Y., Bao, W., Li, C., & Yuan, D. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.009 · Cited by 10
E2EC: Edge-to-Edge Collaboration for Efficient Real-Time Video Surveillance Inference
Guo, L., Zeng, J., Peng, Z., Liang, Y., Zheng, X., & Wang, T. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3559919 · Cited by 10
Dynamic Path Based DNN Synergistic Inference Acceleration in Edge Computing Environment
Meng, Z., Zhou, B., Wang, H., Dong, F., & Zhao, W. (2021) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads53394.2021.00076 · Cited by 10
Edge Intelligence based Co-training of CNN
Xie, F., Xu, A., Jiang, Y., Chen, S., Liao, R., & Wen, H. (2019) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2019.8845531 · Cited by 10
Zhang, P., Tian, H., Luo, H., Li, X., & Nie, G. (2023) · Physical Communication · doi:10.1016/j.phycom.2023.102129 · Cited by 12
Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator
Cao, Z., Cheng, Y., Zhou, Z., Chen, Y., Hu, Y., Lu, A., Liu, J., & Li, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3474743 · Cited by 14
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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