Survey of Collaborative Inference for Edge Intelligence

Wang Rui; Qi Jianpeng; Chen Liang; Yang Long

doi:10.7544/issn1000-1239.202110867

Journal of Computer Research and Development > 2023 > 60(2): 398-414. > DOI: 10.7544/issn1000-1239.202110867 CSTR: 32373.14.issn1000-1239.202110867

Wang Rui, Qi Jianpeng, Chen Liang, Yang Long. Survey of Collaborative Inference for Edge Intelligence[J]. Journal of Computer Research and Development, 2023, 60(2): 398-414. DOI: 10.7544/issn1000-1239.202110867

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Survey of Collaborative Inference for Edge Intelligence

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083
2.
Shunde Graduate School of University of Science and Technology Beijing, Foshan, Guangdong 528300

Funds: This work was supported by the National Natural Science Foundation of China (62173158,72004147).

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Received Date: August 25, 2021
Revised Date: April 14, 2022
Available Online: February 10, 2023
Published Date: August 25, 2021

Graphical Abstract

Abstract

Abstract

At present, the continuous change of information technology along with the dramatic explosion of data quantity makes the cloud computing solutions face many problems such as high latency, limited bandwidth, high carbon footprint, high maintenance cost, and privacy concerns. In recent years, the emergence and rapid development of edge computing has effectively alleviated such dilemmas, sinking user demand processing to the edge and avoiding the flow of massive data in the network. As a typical scenario of edge computing, edge intelligence is gaining increasing attention, in which one of the most important stages is the inference phase. Due to the general low performance of resources in edge computing, collaborative inference through resources is becoming a hot topic. By analyzing the trends of edge intelligence development, we conclude that collaborative inference at the edge is still in the increasing phase and has not yet entered a stable phase. We divide edge-edge collaborative inference into two parts: Intelligent methods and collaborative inference architecture, based on a thorough investigation of edge collaborative inference. The involved key technologies are summarized vertically and organized from the perspective of dynamic scenarios. Each key technology is analyzed in more detail, and the different key technologies are compared horizontally and analyzed on the application scenarios. Finally, we propose several directions that deserve further studying in collaborative edge inference in dynamic scenarios.
- edge computing,
- edge intelligence,
- machine learning,
- edge collaborative inference,
- dynamic scenario

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References (128)

References

[1]	David S, David C, Nick J. Top 10 strategic technology trends for 2020[EB/OL]. (2019-10-20)[2022-02-05].https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020
[2]	Carrie M, David R, Michael S. The growth in connected IoT devices is expected to generate 79.4ZB of data in 2025, according to a new IDC forecast[EB/OL]. (2019-06-18) [2022-02-15].https://www.businesswire.com/news/home/20190618005012/en/The-Growth-in-Connected-IoT-Devices-is-Expected-to-Generate-79.4ZB-of-Data-in-2025-According-to-a-New-IDC-Forecast
[3]	Xiao Yinhao, Jia Yizhen, Liu Chunchi, et al. Edge computing security: State of the art and challenges[J]. Proceedings of the IEEE, 2019, 107(8): 1608−1631 doi: 10.1109/JPROC.2019.2918437
[4]	Kevin M, Amir E. AWS customers rack up hefty bills for moving data[EB/OL]. (2019-10-21)[2022-02-15].https://www.theinformation.com/articles/aws-customers-rack-up-hefty-bills-for-moving-data
[5]	Jin Hai, Jia Lin, Zhou Zhi. Boosting edge intelligence with collaborative cross-edge analytics[J]. IEEE Internet of Things Journal, 2020, 8(4): 2444−2458
[6]	Xiang Chong, Wang Xinyu, Chen Qingrong, et al. No-jump-into-latency in China's Internet! toward last-mile hop count based IP geo-localization[C/OL] //Proc of the 19th Int Symp on Quality of Service. New York: ACM, 2019[2021-03-15].https://doi.org/10.1145/3326285.3329077
[7]	Jiang Xiaolin, Shokri-Ghadikolaei H, Fodor G, et al. Low-latency networking: Where latency lurks and how to tame it[J]. Proceedings of the IEEE, 2018, 107(2): 280−306
[8]	施巍松,张星洲,王一帆,等. 边缘计算: 现状与展望[J]. 计算机研究与发展,2019,56(1):69−89 Shi Weisong, Zhang Xingzhou, Wang Yifan, et al. Edge computing: Status quo and prospect[J]. Journal of Computer Research and Development, 2019, 56(1): 69−89 (in Chinese)
[9]	Zamora-Izquierdo MA, Santa J, Martínez JA, et al. Smart farming IoT platform based on edge and cloud computing[J]. Biosystems Engineering, 2019, 177(1): 4−17
[10]	肖文华,刘必欣,刘巍,等. 面向恶劣环境的边缘计算综述[J]. 指挥与控制学报,2019,5(3):181−190 Xiao Wenhua, Liu Bixin, Liu Wei, et al. A review of edge computing for harsh environments[J]. Journal of Command and Control, 2019, 5(3): 181−190 (in Chinese)
[11]	Stojkoska BLR, Trivodaliev KV. A review of Internet of things for smart home: Challenges and solutions[J]. Journal of Cleaner Production, 2017, 140(3): 1454−1464
[12]	Wan Shaohua, Gu Zonghua, Ni Qiang. Cognitive computing and wireless communications on the edge for healthcare service robots[J]. Computer Communications, 2020, 149(1): 99−106
[13]	吕华章,陈丹,范斌,等. 边缘计算标准化进展与案例分析[J]. 计算机研究与发展,2018,55(3):487−511 Lü Huazhang, Chen Dan, Fan Bin, et al. Standardization progress and case analysis of edge computing[J]. Journal of Computer Research and Development, 2018, 55(3): 487−511 (in Chinese)
[14]	Qi Jianpeng. Awesome edge computing[EB/OL]. (2003-06-02) [2022-03-15]. https://github.com/qijianpeng/awesome-edge-computing#engine
[15]	Cheol-Ho H, Blesson V. Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms[J]. ACM Computing Surveys, 2019, 52(5): 1−37 doi: 10.1145/3342101
[16]	曾鹏,宋纯贺. 边缘计算[J]. 中国计算机学会通讯,2020,16(1):8−10 Zeng Peng, Song Chunhe. Edge computing[J]. Communications of China Computer Federation, 2020, 16(1): 8−10 (in Chinese)
[17]	高晗,田育龙,许封元,等. 深度学习模型压缩与加速综述[J]. 软件学报,2021,32(1):68−92 Gao Han, Tian Yulong, Xu Fengyuan, et al. Overview of deep learning model compression and acceleration[J]. Journal of Software, 2021, 32(1): 68−92 (in Chinese)
[18]	Zhou Zhi, Chen Xu, Li En, 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
[19]	李肯立,刘楚波. 边缘智能: 现状和展望[J]. 大数据,2019,5(3):69−75 Li Kenli, Liu Chubo. Edge intelligence: Status quo and prospect[J]. Big Data, 2019, 5(3): 69−75 (in Chinese)
[20]	谈海生,郭得科,张弛,等. 云边端协同智能边缘计算的发展与挑战[J]. 中国计算机学会通讯,2020,16(1):38−44 Tan Haisheng, Guo Deke, Zhang Chi, et al. Development and challenges of cloud-edge-device collaborative intelligent edge computing[J]. Communications of China Computer Federation, 2020, 16(1): 38−44 (in Chinese)
[21]	张星洲,鲁思迪,施巍松. 边缘智能中的协同计算技术研究[J]. 人工智能,2019,5(7):55−67 Zhang Xingzhou, Lu Sidi, Shi Weisong. Research on collaborative computing technology in edge intelligence[J]. Artificial Intelligence, 2019, 5(7): 55−67 (in Chinese)
[22]	王晓飞. 智慧边缘计算: 万物互联到万物赋能的桥梁[J]. 人民论坛·学术前沿,2020(9):6−17 Wang Xiaofei. Smart edge computing: The bridge from the Internet of everything to the empowerment of everything[J]. People’s Forum·Academic Frontiers, 2020(9): 6−17 (in Chinese)
[23]	Fan Zhenyu, Wang Yang, Fan Wu, et al. Serving at the edge: An edge computing service architecture based on ICN[J]. ACM Transactions on Internet Technology, 2021, 22(1): 1−27
[24]	Jennings A , Copenhagen R V , Rusmin T. Aspects of network edge intelligence[R/OL]. 2001 [2022-03-16]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.20.6997&rep=rep1&type=pdf
[25]	Romaniuk R S. Intelligence in optical networks[G] //Proceedings of SPIE 5125: Proc of the Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments. Bellingham, WA: SPIE, 2003: 17−31
[26]	Okagawa T, Nishida K, Yabusaki M. A proposed mobility management for IP-based IMT network platform[J]. IEICE Transactions on Communications, 2005, 88(7): 2726−2734
[27]	Liang Ye. Mobile intelligence sharing based on agents in mobile peer-to-peer environment[C] //Proc of the 3rd Int Symp on Intelligent Information Technology and Security Informatics. Piscataway, NJ: IEEE, 2010: 667−670
[28]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84−90
[29]	Szegedy C, Liu Wei, Jia Yangqing, et al. Going deeper with convolutions[C/OL] //Proc of the 28th IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2015 [2022-03-16]. https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html
[30]	Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[EB/OL]. (2016-11-04) [2022-03-16]. https://arxiv.org/abs/1602.07360
[31]	Cao Yu, Chen Songqing, Hou Peng, et al. FAST: A Fog computing assisted distributed analytics system to monitor fall for Stroke mitigation[C] //Proc of the 10th IEEE Int Conf on Networking, Architecture and Storage. Piscataway, NJ: IEEE, 2015: 2−11
[32]	Teerapittayanon S, McDanel B, Kung H T. Distributed deep neural networks over the cloud, the edge and end devices[C] //Proc of the 37th IEEE Int Conf on Distributed Computing Systems. Piscataway, NJ: IEEE, 2017: 328−339
[33]	Wang Xiaofei, Han Yiwen, Wang Chenyang, et al. In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning[J]. IEEE Network, 2019, 33(5): 156−165 doi: 10.1109/MNET.2019.1800286
[34]	Kang Yiping, Johann H, Gao Cao, 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
[35]	Li En, Zhou Zhi, and Chen Xu. Edge intelligence: On-demand deep learning model co-inference with device-edge synergy[C] //Proc of the 2018 Workshop on Mobile Edge Communications. New York, ACM, 2018: 31−36
[36]	李逸楷,张通,陈俊龙. 面向边缘计算应用的宽度孪生网络[J]. 自动化学报,2020,46(10):2060−2071 Li Yikai, Zhang Tong, Chen Junlong. Wide twin networks for edge computing applications[J]. Acta Automatica Sinica, 2020, 46(10): 2060−2071 (in Chinese)
[37]	Al-Rakhami M, Alsahli M, Hassan M M, et al. Cost efficient edge intelligence framework using docker containers[C] //Proc of the 16th IEEE Int Conf on Dependable, Autonomic and Secure Computing. Piscataway, NJ: IEEE, 2018: 800−807
[38]	Al-Rakhami M, Gumaei A, Alsahli M, et al. A lightweight and cost effective edge intelligence architecture based on containerization technology[J]. World Wide Web, 2020, 23(2): 1341−1360 doi: 10.1007/s11280-019-00692-y
[39]	Verbraeken J, Wolting M, Katzy J, et al. A survey on distributed machine learning[J]. ACM Computing Surveys, 2020, 53(2): 1−33 doi: 10.1145/3389414
[40]	杨涛,柴天佑. 分布式协同优化的研究现状与展望[J]. 中国科学:技术科学,2020,50(11):1414−1425 doi: 10.1360/SST-2020-0040 Chai Tianyou, Yang Tao. Research status and prospects of distributed collaborative optimization[J]. Scientia Sinica Technologica, 2020, 50(11): 1414−1425 (in Chinese) doi: 10.1360/SST-2020-0040
[41]	Merenda M, Porcaro C, Iero D. Edge machine learning for AI-enabled IoT devices: A review[J/OL]. Sensors, 2020, 20(9) [2022-03-18]. https://doi.org/10.3390/s20092533
[42]	Véstias M P, Duarte R P, de Sousa J T, et al. Moving deep learning to the edge[J/OL]. Algorithms, 2020, 13(5) [2022-03-18]. https://doi.org/10.3390/a13050125
[43]	Chen Jiasi, Ran Xukan. Deep learning with edge computing: A review[J]. Proceedings of the IEEE, 2019, 107(8): 1655−1674 doi: 10.1109/JPROC.2019.2921977
[44]	洪学海,汪洋. 边缘计算技术发展与对策研究[J]. 中国工程科学,2018,20(2):28−34 Hong Xuehai, Wang Yang. Research on the development and countermeasures of edge computing technology[J]. China Engineering Science, 2018, 20(2): 28−34 (in Chinese)
[45]	Hadidi R, Cao Jiashen, Ryoo M S, et al. Toward collaborative inferencing of deep neural networks on Internet-of-things devices[J]. IEEE Internet of Things Journal, 2020, 7(6): 4950−4960 doi: 10.1109/JIOT.2020.2972000
[46]	Zhao Zhuoran, 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
[47]	Pnevmatikatos D N, Pelcat M, Jung M. Embedded Computer Systems: Architectures, Modeling, and Simulation[M]. Berlin: Springer, 2019
[48]	Mao Jiachen, Chen Xiang, Nixon K W, et al. MoDNN: Local distributed mobile computing system for deep neural network[C] //Proc of the 24th Design, Automation Test in Europe Conf Exhibition. Piscataway, NJ: IEEE, 2017: 1396−1401
[49]	Shan Nanliang, Ye Zecong, Cui Xiaolong. Collaborative intelligence: Accelerating deep neural network inference via device-edge synergy[J/OL]. Secrrity and Communication Networks, 2020 [2022-03-16]. https://doi.org/10.1155/2020/8831341
[50]	Lane N D, Bhattacharya S, Georgiev P, et al. DeepX: A software accelerator for low-power deep learning inference on mobile devices[C/OL] //Proc of the 15th ACM/IEEE Int Conf on Information Processing in Sensor Networks (IPSN). 2016 [2022-04-06]. https://doi.org/10.1109/IPSN.2016.7460664
[51]	Zhou Li, Samavatian M H, Bacha A, et al. Adaptive parallel execution of deep neural networks on heterogeneous edge devices[C] //Proc of the 4th ACM/IEEE Symp on Edge Computing. New York: ACM, 2019: 195−208
[52]	Jahierpagliari D, Chiaro R, Macii E, et al. CRIME: Input-dependent collaborative inference for recurrent neural networks[J]. IEEE Transactions on Computers, 2020, 70(10): 1626−1639
[53]	Zhang Shuai, Zhang Sheng, Qian Zhuzhong, et al. DeepSlicing: Collaborative and adaptive CNN inference with low latency[J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 22(9): 2175−2187
[54]	Li En, Zeng Liekang, Zhou Zhi, et al. Edge AI: On-demand accelerating deep neural network inference via edge computing[J]. IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2020, 19(1): 447−457
[55]	Zhang Saiqian, Lin Jieyu, Zhang Qi. Adaptive distributed convolutional neural network inference at the network edge with ADCNN[C/OL] //Proc of the 49th Int Conf on Parallel Processing. 2020 [2022-03-18]. https://doi.org/10.1145/3404397.3404473
[56]	Shao Jiawei, Zhang Jun. BottleNet++: An end-to-end approach for feature compression in device-edge co-inference systems[C/OL] //Proc of the IEEE Int Conf on Communications Workshops. Piscataway, NJ: IEEE, 2020 [2022-03-18]. https://doi.org/10.1109/ICCWorkshops49005.2020.9145068
[57]	Shao Jiawei, Zhang Jun. Communication-computation trade-off in resource-constrained edge inference[J]. IEEE Communications Magazine, 2020, 58(12): 20−26 doi: 10.1109/MCOM.001.2000373
[58]	Avasalcai C, Tsigkanos C, Dustdar S. Resource management for latency-sensitive IoT applications with satisfiability[J/OL]. IEEE Transactions on Services Computing, 2021 [2022-03-18]. https://doi.ieeecomputersociety.org/10.1109/TSC.2021.3074188
[59]	Chen Min, Li Wei, Hao Yiyue, et al. Edge cognitive computing based smart healthcare system[J]. Future Generation Computer Systems, 2018, 86(9): 403−411
[60]	Hu Diyi, Krishnamachari B. Fast and accurate streaming cnn inference via communication compression on the edge[C] //Proc of the 5th ACM/IEEE Int Conf on Internet of Things Design and Implementation. Piscataway, NJ: IEEE, 2020: 157−163
[61]	Hsu K J, Choncholas J, Bhardwaj K, et al. DNS does not suffice for MEC-CDN[C] //Proc of the 19th ACM Workshop on Hot Topics in Networks. New York: ACM, 2020: 212−218
[62]	Campolo C, Lia G, Amadeo M, et al. Towards named AI networking: Unveiling the potential of NDN for edge AI[G] //LNCS 12338: Proc of the 19th Int Conf on Ad-Hoc Networks and Wireless. Cham: Springer, 2020: 16−22
[63]	Jiang A H, Wong D L K, Canel C, et al. Mainstream: Dynamic stem-sharing for multi-tenant video processing[C] //Proc of the 2018 USENIX Annual Technical Conf. New York: ACM, 2018: 29−42
[64]	Mhamdi E, Guerraoui R, Rouault S. On the robustness of a neural network[C] //Proc of the 36th IEEE Symp on Reliable Distributed Systems. Piscataway, NJ: IEEE, 2017: 84−93
[65]	Yousefpour A, Devic S, Nguyen B Q, et al. Guardians of the Deep Fog: Failure-resilient DNN inference from edge to cloud[C] //Proc of the 1st Int Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. New York: ACM, 2019: 25−31
[66]	Hu Chuang, Bao Wei, Wang Dan, et al. Dynamic adaptive DNN surgery for inference acceleration on the edge[C] //Proc of the 38th IEEE Conf on Computer Communications. Piscataway, NJ: IEEE, 2019: 1423−1431
[67]	Song Han, Mao Huizi, Dally W J. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding[EB/OL]. (2016-02-15) [2022-03-18]. https://arxiv.org/abs/1510.00149
[68]	Masana M, van de Weijer J, Herranz L, et al. Domain-adaptive deep network compression[C] //Proc of the IEEE Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2017: 22−29
[69]	Courbariaux M, Bengio Y, David J P. BinaryConnect: Training deep neural networks with binary weights during propagations[C] //Proc of the 28th Int Conf on Neural Information Processing Systems. Cambridge, MA: MIT Press, 2015: 3123−3131
[70]	Gholami A, Kim S, Zhen Dong, et al. A survey of quantization methods for efficient neural network inference[J]. arXiv preprint, arXiv: 2103.13630, 2021
[71]	Cao Qingqing, Irimiea A E, Abdelfattah M, et al. Are mobile DNN accelerators accelerating DNNs?[C] //Proc of the 5th Int Workshop on Embedded and Mobile Deep Learning. New York: ACM, 2021: 7−12
[72]	Guo Kaiyuan, Song Han, Song Yao, et al. Software-hardware codesign for efficient neural network acceleration[J]. IEEE Micro, 2017, 37(2): 18−25 doi: 10.1109/MM.2017.39
[73]	Guo Kaiyuan, Li Wenshuo, Zhong Kai, et al. Neural network accelerator comparison[EB/OL]. (2018-01-01) [2022-12-26]. https://nicsefc.ee.tsinghua.edu.cn/projects/neural-network-accelerator tsinghua.edu.cn/project.html
[74]	Li Hao, Kadav A, Durdanovic I, et al. Pruning filters for efficient convnets[J]. arXiv preprint, arXiv: 1608.08710, 2017
[75]	Luo Jianhao, Zhang Hao, Zhou Hongyu, et al. ThiNet: Pruning cnn filters for a thinner net[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(10): 2525−2538 doi: 10.1109/TPAMI.2018.2858232
[76]	He Yihui, Zhang Xianyu, Sun Jian. Channel pruning for accelerating very deep neural networks[C] //Proc of the 16th IEEE Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2017: 1398−1406
[77]	Hu Hengyuan, Peng Rui, Tai Y W, et al. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures[J]. arXiv preprint, arXiv: 1607.03250, 2016
[78]	Wen Wei, Wu Chunpeng, Wang Yandan, et al. Learning structured sparsity in deep neural networks[C] //Proc of the 30th Int Conf on Neural Information Processing Systems. New York: ACM, 2016: 2082−2090
[79]	Chen Hanting, Wang Yunhe, Xu Chang, et al. Data-free learning of student networks[C] //Proc of the 17th IEEE/CVF Int Conf on Computer Vision. Piscataway, NJ: IEEE, 2019: 3513−3521
[80]	Niu Wei, Ma Xiaolong, Lin Sheng, et al. PatDNN: Achieving real-time DNN execution on mobile devices with pattern-based weight pruning[C] //Proc of the 25th Int Conf on Architectural Support for Programming Languages and Operating Systems. New York: ACM, 2020: 907−922
[81]	Qin Haotong, Gong Ruihao, Liu Xianglong, et al. Binary neural networks: A survey [J]. Pattern Recognition, 2020, 105(9): 107281
[82]	卢冶,龚成,李涛. 深度神经网络压缩自动化的挑战与机遇[J]. 中国计算机学会通讯,2021,17(3):41−47 Lu Ye, Gong Cheng, Li Tao. Challenges and opportunities of deep neural network compression automation[J]. China Computer Society Communications, 2021, 17(3): 41−47 (in Chinese)
[83]	Hubara I, Courbariaux M, Soudry D, et al. Binarized neural networks[C] //Proc of the 30th Int Conf on Neural Information Processing Systems. New York: ACM, 2016: 4114−4122
[84]	Li Fengfu, Liu Bin. Ternary weight networks[J]. arXiv preprint, arXiv: 1605.04711, 2016
[85]	Alemdar H, Leroy V, Prost-Boucle A, et al. Ternary neural networks for resource-efficient AI applications[C] //Proc of the 30th Int Joint Conf on Neural Networks. Piscataway, NJ: IEEE, 2017: 2547−2554
[86]	Chen Yao, Zhang Kang, Gong Cheng, et al. T-DLA: An open-source deep learning accelerator for ternarized DNN models on embedded FPGA[C] //Proc of the 14th IEEE Computer Society Annual Symp on VLSI. Piscataway, NJ: IEEE, 2019: 13−18
[87]	Zhou Shuchuang, Wu Yuxin, Ni Zekun, et al. DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients[J]. arXiv preprint, arXiv: 1606.06160, 2018
[88]	Wang Peisong, Hu Qinghao, Zhang Yifan, et al. Two-step quantization for low-bit neural networks[C] //Proc of the 31st IEEE Conf on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2018: 4376−4384
[89]	Jung Sangli, Son Changyong, Lee Seohyung, et al. Learning to quantize deep networks by optimizing quantization intervals with task loss[C] //Proc of the 32nd IEEE/CVF Conf on Computer Vision and Pattern Recognition . Piscataway, NJ: IEEE, 2019: 4345−4354
[90]	Gong Cheng, Li Tao, Lu Ye, et al. µL2Q: An ultra-low loss quantization method for DNN compression[C/OL] //Proc of the Int Joint Conf on Neural Networks. Piscataway, NJ: IEEE, 2019 [2022-04-07]. https://doi.org/10.1109/IJCNN.2019.8851699
[91]	葛道辉,李洪升,张亮,等. 轻量级神经网络架构综述[J]. 软件学报,2020,31(9):2627−2653 doi: 10.13328/j.cnki.jos.005942 Ge Daohui, Li Hongsheng, Zhang Liang, et al. A review of lightweight neural network architecture[J]. Journal of Software, 2020, 31(9): 2627−2653 (in Chinese) doi: 10.13328/j.cnki.jos.005942
[92]	Shi Lei, Feng Shi, Zhu Zhifang. Functional hashing for compressing neural networks[J]. arXiv preprint, arXiv: 1605.06560, 2016
[93]	Wu Junru, Wang Yue, Wu Zhenyu, et al. Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions[C] //Proc of the 35th Int Conf on Machine Learning PMLR. New York: ACM, 2018: 5363−5372
[94]	Xu Xiaowei, Lu Qing, Wang Tianchen, et al. Efficient hardware implementation of cellular neural networks with incremental quantization and early exit[J]. ACM Journal on Emerging Technologies in Computing Systems, 2018, 14(4): 1−20
[95]	Li Yuhong, Hao Cong, Zhang Xiaofan, et al. EDD: Efficient differentiable DNN architecture and implementation co-search for embedded AI solutions[C/OL] //Proc of the 57th ACM/IEEE Design Automation Conf. New York: ACM, 2020 [2022-04-07]. https://doi.org/10.1109/DAC18072.2020.9218749
[96]	Aimar A, Mostafa H, Calabrese E, et al. NullHop: A flexible convolutional neural network accelerator based on sparse representations of feature maps[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(3): 644−656 doi: 10.1109/TNNLS.2018.2852335
[97]	Sebastian A, Le Gallo M, Khaddam-Aljameh R, et al. Memory devices and applications for in-memory computing[J]. Nature Nanotechnology, 2020, 15(7): 529−544 doi: 10.1038/s41565-020-0655-z
[98]	Song Zhuoran, Fu Bangqi, Wu Feiyang, et al. DRQ: Dynamic region-based quantization for deep neural network acceleration[C] //Proc of the 47th ACM/IEEE Annual Int Symp on Computer Architecture. New York: ACM, 2020: 1010−1021
[99]	Yang Yixiong, Yuan Zhe, Su Fang, et al. Multi-channel precision-sparsity-adapted Inter-frame differential data Codec for video neural network processor[C] //Proc of the 33rd ACM/IEEE Int Symp on Low Power Electronics and Design. New York: ACM, 2020: 103−108
[100]	Tang Yibin, Wang Ying, Li Huawei, et al. MV-Net: Toward real-time deep learning on mobile GPGPU systems[J]. ACM Journal on Emerging Technologies in Computing Systems, 2019, 15(4): 1−25
[101]	Chen Shengbo, Shen Cong, Zhang Lanxue, et al. Dynamic aggregation for heterogeneous quantization in federated learning[J]. IEEE Transactions on Wireless Communications, 2021, 20(10): 6804−6819 doi: 10.1109/TWC.2021.3076613
[102]	Teerapittayanon S, McDanel B, Kung H T. BranchyNet: Fast inference via early exiting from deep neural networks[C] //Proc of the 23rd Int Conf on Pattern Recognition. Piscataway, NJ: IEEE, 2016: 2464−2469
[103]	Lo C, Su YY, Lee CY, et al. A dynamic deep neural network design for efficient workload allocation in edge computing[C] //Proc of the 35th 2017 IEEE Int Conf on Computer Design. Piscataway, NJ: IEEE, 2017: 273−280
[104]	Wang Zizhao, Bao Wei, Yuan Dong, et al. SEE: Scheduling early exit for mobile DNN inference during service outage[C] //Proc of the 22nd Int ACM Conf on Modeling, Analysis and Simulation of Wireless and Mobile Systems. New York: ACM, 2019: 279−288
[105]	Wang Zizhao, Bao Wei, Yuan Dong, et al. Accelerating on-device DNN inference during service outage through scheduling early exit[J]. Computer Communications, 2020, 162(10): 69−82
[106]	Scarpiniti M, Baccarelli E, Momenzadeh A, et al. DeepFogSim: A toolbox for execution and performance evaluation of the inference phase of conditional deep neural networks with early exits atop distributed Fog platforms[J/OL]. Applied Sciences, 2021, 11(1)[2022-03-18]. https://doi.org/10.3390/app11010377
[107]	Su Xiao. EasiEI simulator[CP/OL]. [2022-03-18]. https://gitlab.com/Mirrola/ns-3-dev/-/wikis/EasiEI-Simulator
[108]	Park E, Kim D, Kim S, et al. Big/little deep neural network for ultra low power inference[C] //Proc of the Int Conf on Hardware/Software Codesign and System Synthesis. Piscataway, NJ: IEEE, 2015: 124−132
[109]	Putra T A, Leu J S. Multilevel Neural network for reducing expected inference time[J]. IEEE Access, 2019, 7(11): 174129−174138
[110]	Taylor B, Marco V S, Wolff W, et al. Adaptive deep learning model selection on embedded systems[J]. ACM SIGPLAN Notices, 2018, 53(6): 31−43 doi: 10.1145/3299710.3211336
[111]	Shu Guansheng, Liu Weiqing, Zheng Xiaojie, et al. IF-CNN: Image-aware inference framework for cnn with the collaboration of mobile devices and cloud[J]. IEEE Access, 2018, 6(10): 68621−68633
[112]	Stamoulis D, Chin T W, Prakash A K, et al. Designing adaptive neural networks for energy-constrained image classification[C] //Proc of the Int Conf on Computer-Aided Design. New York: ACM, 2018: 1−8
[113]	Song Mingcong, Zhong Kan, Zhang Jiaqi, et al. In-Situ AI: Towards autonomous and incremental deep learning for IoT systems[C] //Proc of the 24th IEEE Int Symp on High Performance Computer Architecture. Piscataway, NJ: IEEE, 2018: 92−103
[114]	Zhang Li, Han Shihao, Wei Jianyu, et al. nn-Meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices[C] //Proc of the 19th Annual Int Conf on Mobile Systems, Applications, and Services. New York: ACM, 2021: 81−93
[115]	Yue Zhifeng, Zhu Zhixiang, Wang Chuang, et al. Research on big data processing model of edge-cloud collaboration in cyber-physical systems[C] //Proc of the 5th IEEE Int Conf on Big Data Analytics. Piscataway, NJ: IEEE, 2020: 140−144
[116]	Wang Huitian, Cai Guangxing, Huang Zhaowu, et al. ADDA: Adaptive distributed DNN inference acceleration in edge computing environment[C] //Proc of the 25th Int Conf on Parallel and Distributed Systems. Piscataway, NJ: IEEE, 2019: 438−445
[117]	Chen Liang, Qi Jiapeng, Su Xiao, et al. REMR: A reliability evaluation method for dynamic edge computing network under time constraints[J]. arXiv preprint, arXiv: 2112.01913, 2021
[118]	Long Saiqin, Long Weifan, Li Zhetao, et al. A game-based approach for cost-aware task assignment with QoS constraint in collaborative edge and cloud environments[J]. IEEE Transactions on Parallel and Distributed Systems, 2021, 32(7): 1629−1640 doi: 10.1109/TPDS.2020.3041029
[119]	Yang Bo, Cao Xuelin, Li Xiangfan, et al. Mobile-edge-computing-based hierarchical machine learning tasks distribution for IIoT[J]. IEEE Internet of Things Journal, 2020, 7(3): 2169−2180 doi: 10.1109/JIOT.2019.2959035
[120]	Fang Yihao, Jin Ziyi, Zheng Rong. TeamNet: A collaborative inference framework on the edge[C] //Proc of the 39th IEEE Int Conf on Distributed Computing Systems. Piscataway, NJ: IEEE, 2019: 1487−1496
[121]	Fang Yihao, Shalmani SM, Zheng Rong. CacheNet: A model caching framework for deep learning inference on the edge[J]. 2020 (2020-07-03)[2022-03-17]. arXiv preprint, arXiv: 2007.01793, 2020
[122]	檀超,张静宣,王铁鑫,等. 复杂软件系统的不确定性[J]. 软件学报,2021,32(7):1926−1956 doi: 10.13328/j.cnki.jos.006267 Tan Chao, Zhang Jingxuan, Wang Tiexin, et al. Uncertainty in complex software systems[J]. Journal of Software, 2021, 32(7): 1926−1956 (in Chinese) doi: 10.13328/j.cnki.jos.006267
[123]	宋纯贺,曾鹏,于海斌. 工业互联网智能制造边缘计算: 现状与挑战[J]. 中兴通讯技术,2019,25(3):50−57 doi: 10.12142/ZTETJ.201903008 Song Chunhe, Zeng Peng, Yu Haibin. Industrial Internet intelligent manufacturing edge computing: Current situation and challenges[J]. ZTE Technology, 2019, 25(3): 50−57 (in Chinese) doi: 10.12142/ZTETJ.201903008
[124]	Chen Chao, Zhang Daqing, Wang Yasha, et al. Enabling Smart Urban Services with GPS Trajectory Data[M]. Berlin: Springer, 2021
[125]	黄倩怡,李志洋,谢文涛,等. 智能家居中的边缘计算[J]. 计算机研究与发展,2020,57(9):1800−1809 doi: 10.7544/issn1000-1239.2020.20200253 Huang Qianyi, Li Zhiyang, Xie Wentao, et al. Edge computing in smart home[J]. Journal of Computer Research and Development, 2020, 57(9): 1800−1809 (in Chinese) doi: 10.7544/issn1000-1239.2020.20200253
[126]	Li Xian, Bi Suzhi, Wang Hui. Optimizing resource allocation for joint AI model training and task inference in edge intelligence systems[J]. IEEE Wireless Communications Letters, 2021, 10(3): 532−536 doi: 10.1109/LWC.2020.3036852
[127]	Trivedi A, Wang Lin, Bal H, et al. Sharing and caring of data at the edge[C/OL] //Proc of the 3rd USENIX Workshop on Hot Topics in Edge Computing. Berkeley, CA: USENIX Association, 2020 [2022-04-06]. https://www.usenix.org/conference/hotedge20/presentation/trivedi
[128]	Richins D, Doshi D, Blackmore M, et al. AI tax: The hidden cost of AI data center applications[J]. ACM Transactions on Computer Systems, 2021, 37(1-4): 1-32

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