Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Abstract
The computation for today's intelligent personal assistants such as Apple Siri, Google Now, and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires significant amounts of data to be sent to the cloud over the wireless network and puts significant computational pressure on the datacenter. However, as the computational resources in mobile devices become more powerful and energy efficient, questions arise as to whether this cloud-only processing is desirable moving forward, and what are the implications of pushing some or all of this compute to the mobile devices on the edge.
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DL services deployed close to requesting users, with the cloud only participating when additional processing is required, significantly reduce the latency and cost of sending data to the cloud for processing.
“1) DL services are deployed close to the requesting users, and the cloud only participates when additional processing is required [12], hence significantly reducing the latency and cost of sending data to the cloud for processing;”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [12]
For computation-intensive tasks, segmenting and dispatching them to the end, edge and cloud reduces execution delay while ensuring accuracy.
“For a computation-intensive task, it will be reasonably segmented and dispatched separately to the end, edge and cloud for execution, reducing the execution delay of the task while ensuring the accuracy of the results [12], [49], [50].”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [12]
Collaborative end-edge-cloud computing for DL is emerging as an important trend.
“Therefore, collaborative end-edge-cloud computing for DL [12], abstracted in Fig. 5, is emerging as an important trend as depicted in Fig. 6.”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [12]
Neurosurgeon profiles all layers of a DNN on end device and edge node to generate performance prediction models, and uses them with wireless conditions and server load to partition the DNN at the best point for latency or energy.
“In [12], all layers of a DNN are profiled on the end device and the edge node in terms of the data and computation characteristics, in order to generate performance prediction models.Based on these prediction models, wireless conditions and server load levels, the proposed Neurosurgeon evaluates each candidate point in terms of end-to-end latency or mobile energy consumption and partition the DNN”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [12]
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 · see citing works
MAUI: making smartphones last longer with code offload
Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814441 · Cited by 2,281 · see citing works
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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 · see citing works
“For the model partition between the server and the device, Neurosurgeon [79] represents an iconic effort.”
BackgroundInfluentialDeep Learning With Edge Computing: A Review
Chen, J., & Ran, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2921977 · Cited by 1,540 · see citing works
Convergence 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 · see citing works
“For a computation-intensive task, it will be reasonably segmented and dispatched separately to the end, edge and cloud for execution, reducing the execution delay of the task while ensuring the accuracy of the results [12], [49], [50].”
BackgroundMethodInfluentialEdge Machine Learning for AI-Enabled IoT Devices: A Review
Merenda, M., Porcaro, C., & Iero, D. (2020) · Sensors · doi:10.3390/s20092533 · Cited by 523 · see citing works
DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters
Zhao, Z., Barijough, K. M., & Gerstlauer, A. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2858384 · Cited by 502 · see citing works
Communication-efficient edge ai: algorithms and systems
Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3007787 · Cited by 488 · see citing works
Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge
Hu, C., Bao, W., Wang, D., & Liu, F. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737614 · Cited by 416 · see citing works
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
Li, E., Zhou, Z., & Chen, X. (2018) · Proceedings of the 2018 Workshop on Mobile Edge Communications · doi:10.1145/3229556.3229562 · Cited by 385 · see citing works
Model inversion attacks against collaborative inference
He, Z., Zhang, T., & Lee, R. B. (2019) · Proceedings of the 35th Annual Computer Security Applications Conference · doi:10.1145/3359789.3359824 · Cited by 313 · see citing works
Deep learning for edge computing applications: A State-of-the-Art survey
Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982411 · Cited by 259 · see citing works
BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems
Shao, J., & Zhang, J. (2020) · 2020 IEEE International Conference on Communications Workshops (ICC Workshops) · doi:10.1109/iccworkshops49005.2020.9145068 · Cited by 200 · see citing works
Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management
Li, C., Xue, Y., Wang, J., Zhang, W., & Li, T. (2019) · ACM Computing Surveys · doi:10.1145/3154815 · Cited by 180 · see citing works
JALAD: Joint Accuracy-And Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Li, H., Hu, C., Jiang, J., Wang, Z., Wen, Y., & Zhu, W. (2018) · 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/padsw.2018.8645013 · Cited by 160 · see citing works
Zeng, L., Li, E., Zhou, Z., & Chen, X. (2019) · IEEE Network · doi:10.1109/mnet.001.1800506 · Cited by 159 · see citing works
Edge Video Analytics for Public Safety: A Review
Zhang, Q., Sun, H., Wu, X., & Zhong, H. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2925910 · Cited by 156 · see citing works
IONN: Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers
Jeong, H.-J., Lee, H.-J., Shin, C. H., & Moon, S.-M. (2018) · Proceedings of the ACM Symposium on Cloud Computing · doi:10.1145/3267809.3267828 · Cited by 143 · see citing works
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Xu, M., Qian, F., Zhu, M., Huang, F., Pushp, S., & Liu, X. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2893250 · Cited by 138 · see citing works
Characterizing the deployment of deep neural networks on commercial edge devices
Hadidi, R., Cao, J., Xie, Y., Asgari, B., Krishna, T., & Kim, H. (2019) · 2019 IEEE International Symposium on Workload Characterization (IISWC) · doi:10.1109/iiswc47752.2019.9041955 · Cited by 118 · see citing works
Liang, W., Pei, H., Cai, Q., & Wang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2018.10.108 · Cited by 100 · see citing works
Adaptive deep learning model selection on embedded systems
Taylor, B., Marco, V. S., Wolff, W., Elkhatib, Y., & Wang, Z. (2018) · Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems · doi:10.1145/3211332.3211336 · Cited by 90 · see citing works
Zhou, Z., Yang, S., Pu, L., & Yu, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2984332 · Cited by 77 · see citing works
Li, G., Liu, L., Wang, X., Dong, X., Zhao, P., & Feng, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01418-6_40 · Cited by 80 · see citing works
Neural Networks Meet Physical Networks: Distributed Inference Between Edge Devices and the Cloud
Chinchali, S. P., Cidon, E., Pergament, E., Chu, T., & Katti, S. (2018) · Proceedings of the 17th ACM Workshop on Hot Topics in Networks · doi:10.1145/3286062.3286070 · Cited by 46 · see citing works
IF-CNN: Image-Aware Inference Framework for CNN With the Collaboration of Mobile Devices and Cloud
Shu, G., Liu, W., Zheng, X., & Li, J. (2018) · IEEE Access · doi:10.1109/access.2018.2880196 · Cited by 30 · see citing works
A Case for Managed and Model-less Inference Serving
Yadwadkar, N. J., Romero, F., Li, Q., & Kozyrakis, C. (2019) · Proceedings of the Workshop on Hot Topics in Operating Systems · doi:10.1145/3317550.3321443 · Cited by 31 · see citing works
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3055679 · Cited by 25 · see citing works
Exploring the Capabilities of Mobile Devices Supporting Deep Learning
Chen, Y., Biookaghazadeh, S., & Zhao, M. (2018) · Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing · doi:10.1145/3220192.3220460 · Cited by 11 · see citing works
A Locally Distributed Mobile Computing Framework for DNN based Android Applications
Zhang, J., Chen, S., Liu, B., Ma, Y., & Chen, X. (2018) · Proceedings of the Tenth Asia-Pacific Symposium on Internetware · doi:10.1145/3275219.3275236 · Cited by 7 · see citing works
- Authors
- Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L.
- Affiliations
- University of Michigan (US)
- Venue
- Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems
- Publisher
- ACM
- Published
- 2017-04-04
- Pages
- 615-629
- OpenAlex
- W2605258629
- Cited by
- 673
- References
- 2
- Citations by year
- 2026: 53 · 2025: 48 · 2024: 25 · 2023: 51 · 2022: 57 · 2021: 143 · 2020: 152 · 2019: 90 · 2018: 45 · 2017: 9
- FWCI
- 64.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (publishedVersion)
- Funders
- National Science Foundation (IIS-VEC- 1539011,CCF-SHF-1302682,CNS-CSR-1321047)
- Intel (IIS-VEC-1539011)
- ARM
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- edge computing, mobile devices, cloud computing, virtual assistants, wireless data transmission, energy efficiency, mobile edge computing
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