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This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models. We define on-device AI models as those designed to perform local data processing and inference, emphasizing their characteristics such as real-time performance, resource constraints, and enhanced data privacy. The survey is structured around key themes, including the fundamental concepts of AI models, application scenarios across various domains, and technical challenges faced in edge environments. We also discuss optimization and implementation strategies, such as data preprocessing, model compression, and hardware acceleration, which are essential for effective deployment. Furthermore, we examine the impact of emerging technologies, including edge computing and foundation models, on the evolution of on-device AI models. 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IEEE, 1\u201314."},{"issue":"14","key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"6531","DOI":"10.1073\/pnas.1900949116","article-title":"Toward understanding the impact of artificial intelligence on labor","volume":"116","year":"2019","unstructured":"Morgan R. Frank, David Autor, James E. Bessen, Erik Brynjolfsson, Manuel Cebrian, David J. Deming, Maryann Feldman, Matthew Groh, Jos\u00e9 Lobo, Esteban Moro, and others. 2019. Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences 116, 14 (2019), 6531\u20136539.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"e_1_3_3_49_2","first-page":"12127","article-title":"Fractrain: Fractionally squeezing bit savings both temporally and spatially for efficient DNN training","volume":"33","year":"2020","unstructured":"Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, and Yingyan Lin. 2020. 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EdgeEye: An edge service framework for real-time intelligent video analytics. In Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking. 1\u20136."},{"key":"e_1_3_3_112_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2915983"},{"key":"e_1_3_3_113_2","first-page":"323","volume-title":"Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services","author":"Liu Xiaochen","year":"2018","unstructured":"Xiaochen Liu, Yurong Jiang, Puneet Jain, and Kyu-Han Kim. 2018. TAR: Enabling fine-grained targeted advertising in retail stores. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. 323\u2013336."},{"key":"e_1_3_3_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2946476"},{"key":"e_1_3_3_115_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3004500"},{"key":"e_1_3_3_116_2","first-page":"573","volume-title":"2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS\u201921)","author":"Long Yinghan","year":"2021","unstructured":"Yinghan Long, Indranil Chakraborty, et\u00a0al. 2021. Complexity-aware adaptive training and inference for edge-cloud distributed AI systems. In 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS\u201921). IEEE, 573\u2013583."},{"key":"e_1_3_3_117_2","doi-asserted-by":"crossref","unstructured":"J. Lou Z. Tang and W. Jia. 2022. Energy-efficient joint task assignment and migration in data centers: A deep reinforcement learning approach. 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Resource-constrained neural architecture search on edge devices. IEEE Transactions on Network Science and Engineering 9, 1 (2021), 134\u2013142.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_3_125_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3268339"},{"issue":"8","key":"e_1_3_3_126_2","first-page":"6757","article-title":"Federated data cleaning: Collaborative and privacy-preserving data cleaning for edge intelligence","volume":"8","author":"Ma Lichuan","year":"2020","unstructured":"Lichuan Ma, Qingqi Pei, Lu Zhou, Haojin Zhu, Licheng Wang, and Yusheng Ji. 2020. Federated data cleaning: Collaborative and privacy-preserving data cleaning for edge intelligence. IEEE Internet of Things Journal 8, 8 (2020), 6757\u20136770.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_3_127_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_3_128_2","doi-asserted-by":"crossref","unstructured":"Y. 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In International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=vh-0sUt8HlG"},{"key":"e_1_3_3_133_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3059819"},{"issue":"2","key":"e_1_3_3_134_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3440754","article-title":"Data protection in AI services: A survey","volume":"54","author":"Meurisch Christian","year":"2021","unstructured":"Christian Meurisch and Max M\u00fchlh\u00e4user. 2021. Data protection in AI services: A survey. ACM Computing Surveys (CSUR) 54, 2 (2021), 1\u201338.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_3_135_2","volume-title":"AI and Education: A Guidance for Policymakers","author":"Miao Fengchun","year":"2021","unstructured":"Fengchun Miao, Wayne Holmes, Ronghuai Huang, et\u00a0al. 2021. AI and Education: A Guidance for Policymakers. Unesco Publishing."},{"key":"e_1_3_3_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2023.123561"},{"key":"e_1_3_3_137_2","article-title":"ONNX runtime: Cross-platform, high performance ML inferencing and training accelerator.","year":"2019","unstructured":"Microsoft. 2019. ONNX runtime: Cross-platform, high performance ML inferencing and training accelerator. GitHub Repository (2019). https:\/\/github.com\/microsoft\/onnxruntime","journal-title":"GitHub Repository"},{"key":"e_1_3_3_138_2","doi-asserted-by":"crossref","unstructured":"R. Mishra A. Gupta and H. P. Gupta. 2021. Locomotion mode recognition using sensory data with noisy labels: A deep learning approach. 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Nature 621, 7979 (2023), 467\u2013470.","journal-title":"Nature"},{"issue":"3","key":"e_1_3_3_141_2","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TC.2019.2949300","article-title":"Energy-efficient pattern recognition hardware with elementary cellular automata","volume":"69","author":"Moran Alejandro","year":"2019","unstructured":"Alejandro Moran, Christiam F. Frasser, Miquel Roca, and Josep L. Rossello. 2019. Energy-efficient pattern recognition hardware with elementary cellular automata. IEEE Transactions on Computers 69, 3 (2019), 392\u2013401.","journal-title":"IEEE Transactions on Computers"},{"key":"e_1_3_3_142_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02200-8"},{"key":"e_1_3_3_143_2","doi-asserted-by":"publisher","unstructured":"F. Mou J. Lou Z. Tang Y. Wu W. Jia Y. Zhang and W. Zhao. 2025. Adaptive digital twin migration in vehicular edge computing and networks. IEEE Transactions on Vehicular Technology 74 3 (2025) 4839\u20134854. 10.1109\/TVT.2024.3492349","DOI":"10.1109\/TVT.2024.3492349"},{"key":"e_1_3_3_144_2","doi-asserted-by":"publisher","DOI":"10.1145\/3469029"},{"key":"e_1_3_3_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3160970"},{"key":"e_1_3_3_146_2","first-page":"4448","volume-title":"2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201922)","author":"Ni Jianyuan","year":"2022","unstructured":"Jianyuan Ni, Raunak Sarbajna, Yang Liu, et\u00a0al. 2022. Cross-modal knowledge distillation for vision-to-sensor action recognition. In 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201922). IEEE, 4448\u20134452."},{"key":"e_1_3_3_147_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460288"},{"key":"e_1_3_3_148_2","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378534"},{"key":"e_1_3_3_149_2","first-page":"167","volume-title":"2021 ACM\/IEEE 48th Annual International Symposium on Computer Architecture (ISCA\u201921)","author":"Nori Anant V.","year":"2021","unstructured":"Anant V. Nori, Rahul Bera, et\u00a0al. 2021. Reduct: Keep it close, keep it cool!: Efficient scaling of DNN inference on multi-core cpus with near-cache compute. In 2021 ACM\/IEEE 48th Annual International Symposium on Computer Architecture (ISCA\u201921). IEEE, 167\u2013180."},{"key":"e_1_3_3_150_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2021.102062"},{"key":"e_1_3_3_151_2","article-title":"\\(NVIDIA^{&#x00AE;}\\)  \\(TensorRT^{TM}\\) , an SDK for high-performance deep learning inference.","year":"2017","unstructured":"NVIDIA. 2017. \\(NVIDIA^{&#x00AE;}\\) \\(TensorRT^{TM}\\) , an SDK for high-performance deep learning inference. GitHub Repository (2017). https:\/\/github.com\/NVIDIA\/TensorRT","journal-title":"GitHub Repository"},{"key":"e_1_3_3_152_2","first-page":"7392","volume-title":"International Conference on Machine Learning","author":"Obukhov Anton","year":"2020","unstructured":"Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis, Menelaos Kanakis, Dengxin Dai, and Luc Van Gool. 2020. T-basis: A compact representation for neural networks. In International Conference on Machine Learning. PMLR, 7392\u20137404."},{"key":"e_1_3_3_153_2","doi-asserted-by":"publisher","DOI":"10.1145\/3510832"},{"key":"e_1_3_3_154_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2941458"},{"key":"e_1_3_3_155_2","unstructured":"A. Paszke S. Gross F. Massa A. Lerer J. Bradbury G. Chanan T. Killeen Z. Lin N. Gimelshein L. Antiga A. Desmaison A. K\u00f6pf E. Yang Z. DeVito M. Raison A. Tejani S. Chilamkurthy B. Steiner L. Fang J. Bai and S. Chintala. 2019. PyTorch: an imperative style high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc. Red Hook NY USA 1\u201312."},{"key":"e_1_3_3_156_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102561"},{"issue":"1","key":"e_1_3_3_157_2","doi-asserted-by":"crossref","first-page":"5806","DOI":"10.1038\/s41467-021-25873-0","article-title":"Tree-based machine learning performed in-memory with memristive analog CAM","volume":"12","author":"Pedretti Giacomo","year":"2021","unstructured":"Giacomo Pedretti, Catherine E. Graves, Sergey Serebryakov, Ruibin Mao, Xia Sheng, Martin Foltin, Can Li, and John Paul Strachan. 2021. Tree-based machine learning performed in-memory with memristive analog CAM. 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Springer, 598\u2013615."},{"key":"e_1_3_3_162_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3009103"},{"key":"e_1_3_3_163_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3051080"},{"key":"e_1_3_3_164_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103737"},{"issue":"11","key":"e_1_3_3_165_2","doi-asserted-by":"crossref","first-page":"9010","DOI":"10.1109\/TWC.2022.3171824","article-title":"Caching and computation offloading in high altitude platform station (HAPS) assisted intelligent transportation systems","volume":"21","year":"2022","unstructured":"Qiqi Ren, Omid Abbasi, Gunes Karabulut Kurt, Halim Yanikomeroglu, and Jian Chen. 2022. Caching and computation offloading in high altitude platform station (HAPS) assisted intelligent transportation systems. 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The larger the fairer? Small neural networks can achieve fairness for edge devices. In Proceedings of the 59th ACM\/IEEE Design Automation Conference. 163\u2013168."},{"key":"e_1_3_3_173_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3007787"},{"issue":"17","key":"e_1_3_3_174_2","doi-asserted-by":"crossref","first-page":"16402","DOI":"10.1109\/JIOT.2022.3150386","article-title":"Joint online optimization of data sampling rate and preprocessing mode for edge\u2013cloud collaboration-enabled industrial IoT","volume":"9","author":"Shi You","year":"2022","unstructured":"You Shi, Changyan Yi, Bing Chen, Chenze Yang, Kun Zhu, and Jun Cai. 2022. Joint online optimization of data sampling rate and preprocessing mode for edge\u2013cloud collaboration-enabled industrial IoT. 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In Proceedings of the 29th International Conference on Neural Information Processing Systems-Volume 2. 3088\u20133096."},{"key":"e_1_3_3_177_2","doi-asserted-by":"crossref","first-page":"320","DOI":"10.23919\/FRUCT54823.2022.9770931","volume-title":"2022 31st Conference of Open Innovations Association (FRUCT\u201922)","author":"Sipola Tuomo","year":"2022","unstructured":"Tuomo Sipola, Janne Alatalo, Tero Kokkonen, and Mika Rantonen. 2022. Artificial intelligence in the IoT era: A review of edge AI hardware and software. In 2022 31st Conference of Open Innovations Association (FRUCT\u201922). IEEE, 320\u2013331."},{"key":"e_1_3_3_178_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3061981"},{"key":"e_1_3_3_179_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000372"},{"key":"e_1_3_3_180_2","first-page":"2745","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","year":"2021","unstructured":"Ayush Srivastava, Oshin Dutta, Jigyasa Gupta, Sumeet Agarwal, and Prathosh AP. 2021. A variational information bottleneck based method to compress sequential networks for human action recognition. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 2745\u20132754."},{"key":"e_1_3_3_181_2","doi-asserted-by":"publisher","unstructured":"Y. Sui M. Yin Y. Gong and B. Yuan. 2024. Co-exploring structured sparsification and low-rank tensor decomposition for compact DNNs. 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EfficientNetV2: Smaller models and faster training. In International Conference on Machine Learning. PMLR, 10096\u201310106."},{"issue":"11","key":"e_1_3_3_190_2","first-page":"6327","article-title":"Collective deep reinforcement learning for intelligence sharing in the internet of intelligence-empowered edge computing","volume":"22","year":"2022","unstructured":"Qinqin Tang, Renchao Xie, Fei Richard Yu, Tianjiao Chen, Ran Zhang, Tao Huang, and Yunjie Liu. 2022. Collective deep reinforcement learning for intelligence sharing in the internet of intelligence-empowered edge computing. 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IEEE Transactions on Cloud Computing 11 2 (2022) 1970\u20131984.","DOI":"10.1109\/TCC.2022.3175610"},{"key":"e_1_3_3_196_2","unstructured":"Gemma Team Morgane Riviere Shreya Pathak Pier Giuseppe Sessa Cassidy Hardin Surya Bhupatiraju L\u00e9onard Hussenot Thomas Mesnard Bobak Shahriari Alexandre Ram\u00e9 and others. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118 (2024)."},{"key":"e_1_3_3_197_2","article-title":"NCNN is a high-performance neural network inference framework optimized for the mobile platform.","year":"2017","unstructured":"Tencent. 2017. NCNN is a high-performance neural network inference framework optimized for the mobile platform. GitHub Repository (2017). https:\/\/github.com\/Tencent\/ncnn","journal-title":"GitHub Repository"},{"key":"e_1_3_3_198_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.10.043"},{"key":"e_1_3_3_199_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00821"},{"key":"e_1_3_3_200_2","unstructured":"K. Ullrich E. Meeds and M. Welling. 2017. Soft Weight-sharing for neural network compression. In International Conference on Learning Representations."},{"issue":"66","key":"e_1_3_3_201_2","first-page":"13","article-title":"Dimensionality reduction: A comparative","volume":"10","year":"2009","unstructured":"Laurens Van Der Maaten, Eric Postma, Jaap Van den Herik, and others. 2009. Dimensionality reduction: A comparative. Journal of Machine Learning Research 10, 66-71 (2009), 13.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"e_1_3_3_202_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3433993","article-title":"A deep learning approach for voice disorder detection for smart connected living environments","volume":"22","author":"Verde Laura","year":"2021","unstructured":"Laura Verde, Nadia Brancati, Giuseppe De Pietro, Maria Frucci, and Giovanna Sannino. 2021. A deep learning approach for voice disorder detection for smart connected living environments. ACM Transactions on Internet Technology (TOIT) 22, 1 (2021), 1\u201316.","journal-title":"ACM Transactions on Internet Technology (TOIT)"},{"key":"e_1_3_3_203_2","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1109\/IPDPSW52791.2021.00128","volume-title":"2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW\u201921)","author":"Verma Gaurav","year":"2021","unstructured":"Gaurav Verma, Yashi Gupta, Abid M. Malik, and Barbara Chapman. 2021. Performance evaluation of deep learning compilers for edge inference. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW\u201921). IEEE, 858\u2013865."},{"key":"e_1_3_3_204_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"issue":"1","key":"e_1_3_3_205_2","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/MNET.011.2000249","article-title":"A trusted consensus scheme for collaborative learning in the edge AI computing domain","volume":"35","year":"2021","unstructured":"Ke Wang, Ship Peng Xu, Chien-Ming Chen, SK Hafizul Islam, Mohammad Mehedi Hassan, Claudio Savaglio, Pasquale Pace, and Gianluca Aloi. 2021. A trusted consensus scheme for collaborative learning in the edge AI computing domain. IEEE Network 35, 1 (2021), 204\u2013210.","journal-title":"IEEE Network"},{"key":"e_1_3_3_206_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2022.3182856"},{"issue":"10","key":"e_1_3_3_207_2","first-page":"2254","article-title":"High-throughput CNN inference on embedded arm big. little multicore processors","volume":"39","year":"2019","unstructured":"Siqi Wang, Gayathri Anantanarayanan, Yifan Zeng, Neeraj Goel, Anuj Pathania, and Tulika Mitra. 2019. High-throughput CNN inference on embedded arm big. little multicore processors. IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems 39, 10 (2019), 2254\u20132267.","journal-title":"IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems"},{"key":"e_1_3_3_208_2","first-page":"63","volume-title":"IEEE Conference on Computer Communications (IEEE INFOCOM\u201918)","year":"2018","unstructured":"Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan. 2018. When edge meets learning: Adaptive control for resource-constrained distributed machine learning. In IEEE Conference on Computer Communications (IEEE INFOCOM\u201918). IEEE, 63\u201371."},{"key":"e_1_3_3_209_2","first-page":"2519","volume-title":"IEEE Conference on Computer Communications (IEEE INFOCOM\u201920)","author":"Wang Shibo","year":"2020","unstructured":"Shibo Wang, Shusen Yang, and Cong Zhao. 2020. SurveilEdge: Real-time video query based on collaborative cloud-edge deep learning. In IEEE Conference on Computer Communications (IEEE INFOCOM\u201920). IEEE, 2519\u20132528."},{"key":"e_1_3_3_210_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2938861"},{"key":"e_1_3_3_211_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3184115"},{"key":"e_1_3_3_212_2","doi-asserted-by":"publisher","DOI":"10.1145\/2966986.2967068"},{"key":"e_1_3_3_213_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3184175"},{"key":"e_1_3_3_214_2","volume-title":"TinyML: Machine Learning with Tensorflow Lite on Arduino and Ultra-low-power Microcontrollers","author":"Warden Pete","year":"2019","unstructured":"Pete Warden and Daniel Situnayake. 2019. TinyML: Machine Learning with Tensorflow Lite on Arduino and Ultra-low-power Microcontrollers. O\u2019Reilly Media."},{"key":"e_1_3_3_215_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.11.018"},{"key":"e_1_3_3_216_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-022-00775-9"},{"key":"e_1_3_3_217_2","first-page":"10734","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","year":"2019","unstructured":"Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. 2019. FFNet: Hardware-aware efficient ConvNet design via differentiable neural architecture search. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 10734\u201310742."},{"key":"e_1_3_3_218_2","first-page":"5363","volume-title":"International Conference on Machine Learning","year":"2018","unstructured":"Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, and Yingyan Lin. 2018. Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions. In International Conference on Machine Learning. PMLR, 5363\u20135372."},{"issue":"5","key":"e_1_3_3_219_2","first-page":"3086","article-title":"Edge computing driven low-light image dynamic enhancement for object detection","volume":"10","year":"2022","unstructured":"Yirui Wu, Haifeng Guo, Chinmay Chakraborty, Mohammad R. Khosravi, Stefano Berretti, and Shaohua Wan. 2022. Edge computing driven low-light image dynamic enhancement for object detection. 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IEEE, 392\u2013405."},{"issue":"2","key":"e_1_3_3_223_2","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/TII.2019.2957130","article-title":"An AI-enabled three-party game framework for guaranteed data privacy in mobile edge crowdsensing of IoT","volume":"17","author":"Xiong Jinbo","year":"2019","unstructured":"Jinbo Xiong, Mingfeng Zhao, Md. Zakirul Alam Bhuiyan, Lei Chen, and Youliang Tian. 2019. An AI-enabled three-party game framework for guaranteed data privacy in mobile edge crowdsensing of IoT. IEEE Transactions on Industrial Informatics 17, 2 (2019), 922\u2013933.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_3_3_224_2","article-title":"Edge intelligence: Architectures, challenges, and applications","author":"Xu Dianlei","year":"2020","unstructured":"Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, and Pan Hui. 2020. Edge intelligence: Architectures, challenges, and applications. arXiv preprint arXiv:2003.12172 (2020).","journal-title":"arXiv preprint arXiv:2003.12172"},{"key":"e_1_3_3_225_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3119950"},{"key":"e_1_3_3_226_2","article-title":"On-device language models: A comprehensive review","author":"Xu Jiajun","year":"2024","unstructured":"Jiajun Xu, Zhiyuan Li, Wei Chen, Qun Wang, Xin Gao, Qi Cai, and Ziyuan Ling. 2024. On-device language models: A comprehensive review. arXiv preprint arXiv:2409.00088 (2024).","journal-title":"arXiv preprint arXiv:2409.00088"},{"issue":"2","key":"e_1_3_3_227_2","first-page":"246","article-title":"Directx: Dynamic resource-aware CNN reconfiguration framework for real-time mobile applications","volume":"40","year":"2020","unstructured":"Zirui Xu, Fuxun Yu, Zhuwei Qin, Chenchen Liu, and Xiang Chen. 2020. Directx: Dynamic resource-aware CNN reconfiguration framework for real-time mobile applications. 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In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 678\u2013679."},{"issue":"7","key":"e_1_3_3_230_2","doi-asserted-by":"crossref","first-page":"4178","DOI":"10.1109\/TII.2019.2897128","article-title":"An efficient edge artificial intelligence multipedestrian tracking method with rank constraint","volume":"15","author":"Yang Honghong","year":"2019","unstructured":"Honghong Yang, Jinming Wen, Xiaojun Wu, Li He, and Shahid Mumtaz. 2019. An efficient edge artificial intelligence multipedestrian tracking method with rank constraint. IEEE Transactions on Industrial Informatics 15, 7 (2019), 4178\u20134188.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"e_1_3_3_231_2","first-page":"1134","article-title":"BitSystolic: A 26.7 TOPS\/W 2b~ 8b NPU with configurable data flows for edge devices","volume":"68","author":"Yang Qing","year":"2020","unstructured":"Qing Yang and Hai Li. 2020. BitSystolic: A 26.7 TOPS\/W 2b~ 8b NPU with configurable data flows for edge devices. IEEE Transactions on Circuits and Systems I: Regular Papers 68, 3 (2020), 1134\u20131145.","journal-title":"IEEE Transactions on Circuits and Systems I: Regular Papers"},{"key":"e_1_3_3_232_2","first-page":"25566","volume-title":"International Conference on Machine Learning","author":"You Haoran","year":"2022","unstructured":"Haoran You, Baopu Li, Shi Huihong, Yonggan Fu, and Yingyan Lin. 2022. ShiftAddNAS: Hardware-inspired search for more accurate and efficient neural networks. In International Conference on Machine Learning. PMLR, 25566\u201325580."},{"key":"e_1_3_3_233_2","first-page":"122","volume-title":"Proceedings of the 2020 ACM\/SIGDA International Symposium on Field-programmable Gate Arrays","author":"Yu Yunxuan","year":"2020","unstructured":"Yunxuan Yu, Tiandong Zhao, Kun Wang, and Lei He. 2020. Light-OPU: An FPGA-based overlay processor for lightweight convolutional neural networks. In Proceedings of the 2020 ACM\/SIGDA International Symposium on Field-programmable Gate Arrays. 122\u2013132."},{"key":"e_1_3_3_234_2","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3649361"},{"issue":"6","key":"e_1_3_3_235_2","doi-asserted-by":"crossref","first-page":"5013","DOI":"10.1109\/JIOT.2022.3219202","article-title":"Energy-aware AI-driven framework for edge-computing-based IoT applications","volume":"10","author":"Zawish Muhammad","year":"2022","unstructured":"Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, and Steven Davy. 2022. Energy-aware AI-driven framework for edge-computing-based IoT applications. IEEE Internet of Things Journal 10, 6 (2022), 5013\u20135023.","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_3_236_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.004.2300479"},{"key":"e_1_3_3_237_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2020.101713"},{"key":"e_1_3_3_238_2","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081336"},{"key":"e_1_3_3_239_2","article-title":"Data-centric artificial intelligence: A survey","author":"Zha Daochen","year":"2023","unstructured":"Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu. 2023. Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158 (2023).","journal-title":"arXiv preprint arXiv:2303.10158"},{"key":"e_1_3_3_240_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3154605"},{"issue":"2","key":"e_1_3_3_241_2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/JPROC.2019.2947490","article-title":"Mobile edge intelligence and computing for the internet of vehicles","volume":"108","author":"Zhang Jun","year":"2019","unstructured":"Jun Zhang and Khaled B. Letaief. 2019. Mobile edge intelligence and computing for the internet of vehicles. Proceedings of the IEEE 108, 2 (2019), 246\u2013261.","journal-title":"Proceedings of the IEEE"},{"key":"e_1_3_3_242_2","first-page":"3089","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","author":"Zhang Jie","year":"2018","unstructured":"Jie Zhang, Xiaolong Wang, Dawei Li, and Yalin Wang. 2018. Dynamically hierarchy revolution: Dirnet for compressing recurrent neural network on mobile devices. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3089\u20133096."},{"key":"e_1_3_3_243_2","first-page":"3713","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","year":"2019","unstructured":"Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma. 2019. Be your own teacher: Improve the performance of convolutional neural networks via self distillation. 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Octo: INT8 training with loss-aware compensation and backward quantization for tiny on-device learning. In USENIX Annual Technical Conference. 177\u2013191."},{"key":"e_1_3_3_253_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3063147"},{"key":"e_1_3_3_254_2","first-page":"164","volume-title":"2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS\u201918)","author":"Zhou Sha","year":"2018","unstructured":"Sha Zhou and Lei Zhang. 2018. Smart home electricity demand forecasting system based on edge computing. In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS\u201918). IEEE, 164\u2013167."},{"key":"e_1_3_3_255_2","unstructured":"W. Zhou Y. Jia Y. Yao L. Zhu L. Guan Y. Mao and Y. Zhang. 2019. Discovering and understanding the security hazards in the interactions between IoT devices mobile apps and clouds on smart home platforms. 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