计算机技术、电信技术 |
|
|
|
|
边端融合的终端情境自适应深度感知模型 |
王虹力( ),郭斌*( ),刘思聪,刘佳琪,仵允港,於志文 |
西北工业大学 计算机学院,陕西 西安 710072 |
|
End context-adaptative deep sensing model with edge-end collaboration |
Hong-li WANG( ),Bin GUO*( ),Si-cong LIU,Jia-qi LIU,Yun-gang WU,Zhi-wen YU |
College of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China |
引用本文:
王虹力,郭斌,刘思聪,刘佳琪,仵允港,於志文. 边端融合的终端情境自适应深度感知模型[J]. 浙江大学学报(工学版), 2021, 55(4): 626-638.
Hong-li WANG,Bin GUO,Si-cong LIU,Jia-qi LIU,Yun-gang WU,Zhi-wen YU. End context-adaptative deep sensing model with edge-end collaboration. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 626-638.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.04.004
或
http://www.zjujournals.com/eng/CN/Y2021/V55/I4/626
|
1 |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E Imagenet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012, 25: 1097- 1105
|
2 |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
|
3 |
ALOM M Z, TAHA T M, YAKOPCIC C, et al A state-of-the-art survey on deep learning theory and architectures[J]. Electronics, 2019, 8 (3): 292
doi: 10.3390/electronics8030292
|
4 |
ZHOU Z, CHEN X, LI E, et al Edge intelligence: paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019, 107 (8): 1738- 1762
doi: 10.1109/JPROC.2019.2918951
|
5 |
LUO P, ZHU Z, LIU Z, et al. Face model compression by distilling knowledge from neurons [C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Arizona: AAAI, 2016: 3560-3566.
|
6 |
HAN S, POOL J, TRAN J, et al. Learning both weights and connections for efficient neural networks [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2015: 1135-1143.
|
7 |
LUO J H, WU J, LIN W. Thinet: a filter level pruning method for deep neural network compression [C]// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 5058-5066.
|
8 |
LIU S, LIN Y, ZHOU Z, et al. On-demand deep model compression for mobile devices: a usage-driven model selection framework [C]// Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. Munich: ACM, 2018: 389-400.
|
9 |
ZHAO Z, BARIJOUGH K M, GERSTLAUER A DeepThings: distributed adaptive deep learning inference on resource-constrained IoT edge clusters[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018, 37 (11): 2348- 2359
doi: 10.1109/TCAD.2018.2858384
|
10 |
KANG Y, HAUSWALD J, GAO C, et al Neurosurgeon: collaborative intelligence between the cloud and mobile edge[J]. ACM SIGARCH Computer Architecture News, 2017, 45 (1): 615- 629
doi: 10.1145/3093337.3037698
|
11 |
LI H, HU C, JIANG J, et al. JALAD: joint accuracy-and latency-aware deep structure decoupling for edge-cloud execution [C]// 2018 IEEE 24th International Conference on Parallel and Distributed Systems. Singapore: IEEE, 2018: 671-678.
|
12 |
MAO J, CHEN X, NIXON K W, et al. Modnn: local distributed mobile computing system for deep neural network [C]// Design, Automation and Test in Europe Conference and Exhibition. Lausanne: IEEE, 2017: 1396-1401.
|
13 |
KO J H, NA T, AMIR M F, et al. Edge-host partitioning of deep neural networks with feature space encoding for resource-constrained internet-of-things platforms [C]// 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance. New Zealand: IEEE, 2018: 1-6.
|
14 |
LIU N, MA X, XU Z, et al. AutoCompress: an automatic DNN structured pruning framework for ultra-high compression rates [C]// Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI, 2020, 34(04): 4876-4883.
|
15 |
HE Y, ZHANG X, SUN J. Channel pruning for accelerating very deep neural networks [C]// Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 1389-1397.
|
16 |
HU C, BAO W, WANG D, et al. Dynamic adaptive DNN surgery for inference acceleration on the edge [C]// IEEE Conference on Computer Communications. Paris: IEEE, 2019: 1423-1431.
|
17 |
CHEN Y H, EMER J, SZE V Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks[J]. ACM SIGARCH Computer Architecture News, 2016, 44 (3): 367- 379
doi: 10.1145/3007787.3001177
|
18 |
YANG T J, CHEN Y H, SZE V. Designing energy-efficient convolutional neural networks using energy-aware pruning [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 5687-5695.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|