{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:45:21Z","timestamp":1775745921741,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","award":["GHP\/126\/19SZ"],"award-info":[{"award-number":["GHP\/126\/19SZ"]}],"id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,15]]},"DOI":"10.1145\/3485730.3485938","type":"proceedings-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T11:41:43Z","timestamp":1636630903000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["RT-mDL"],"prefix":"10.1145","author":[{"given":"Neiwen","family":"Ling","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Yuze","family":"He","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Guoliang","family":"Xing","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong SAR, China"}]},{"given":"Daqi","family":"Xie","sequence":"additional","affiliation":[{"name":"Edge Cloud Innovation Lab, Huawei Cloud, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"Abadi Mart\u00edn","year":"2015","unstructured":"Mart\u00edn Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg S. Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Ian Goodfellow , Andrew Harp , Geoffrey Irving , Michael Isard , Yangqing Jia , Rafal Jozefowicz , Lukasz Kaiser , Manjunath Kudlur , Josh Levenberg , Dandelion Man\u00e9 , Rajat Monga , Sherry Moore , Derek Murray , Chris Olah , Mike Schuster , Jonathon Shlens , Benoit Steiner , Ilya Sutskever , Kunal Talwar , Paul Tucker , Vincent Vanhoucke , Vijay Vasudevan , Fernanda Vi\u00e9gas , Oriol Vinyals , Pete Warden , Martin Wattenberg , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . TensorFlow: Large-scale machine learning on heterogeneous systems , 2015 . Software available from tensorflow.org. Mart\u00edn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Man\u00e9, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Vi\u00e9gas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-015-0270-y"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings 22nd IEEE Real-Time Systems Symposium (RTSS 2001)","author":"Aydin Hakan","unstructured":"Hakan Aydin , Rami Melhem , Daniel Moss\u00e9 , and Pedro Mejia-Alvarez . Dynamic and aggressive scheduling techniques for power-aware real-time systems . In Proceedings 22nd IEEE Real-Time Systems Symposium (RTSS 2001) (Cat. No. 01PR1420), pages 95--105. IEEE, 2001. Hakan Aydin, Rami Melhem, Daniel Moss\u00e9, and Pedro Mejia-Alvarez. Dynamic and aggressive scheduling techniques for power-aware real-time systems. In Proceedings 22nd IEEE Real-Time Systems Symposium (RTSS 2001)(Cat. No. 01PR1420), pages 95--105. IEEE, 2001."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2018.00017"},{"key":"e_1_3_2_1_5_1","volume-title":"Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332","author":"Cai Han","year":"2018","unstructured":"Han Cai , Ligeng Zhu , and Song Han . Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 , 2018 . Han Cai, Ligeng Zhu, and Song Han. Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332, 2018."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2018.00021"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2018.00056"},{"key":"e_1_3_2_1_8_1","unstructured":"F1TENTH Community. F1tenth. https:\/\/f1tenth.org\/.  F1TENTH Community. F1tenth. https:\/\/f1tenth.org\/."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01166"},{"key":"e_1_3_2_1_10_1","volume-title":"Fast and accurate model scaling. arXiv preprint arXiv:2103.06877","author":"Doll\u00e1r Piotr","year":"2021","unstructured":"Piotr Doll\u00e1r , Mannat Singh , and Ross Girshick . Fast and accurate model scaling. arXiv preprint arXiv:2103.06877 , 2021 . Piotr Doll\u00e1r, Mannat Singh, and Ross Girshick. Fast and accurate model scaling. arXiv preprint arXiv:2103.06877, 2021."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2018.00019"},{"key":"e_1_3_2_1_12_1","first-page":"953","volume-title":"Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection. In 2017 IEEE winter conference on applications of computer vision (WACV)","author":"Du Xianzhi","year":"2017","unstructured":"Xianzhi Du , Mostafa El-Khamy , Jungwon Lee , and Larry Davis . Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection. In 2017 IEEE winter conference on applications of computer vision (WACV) , pages 953 -- 961 . IEEE , 2017 . Xianzhi Du, Mostafa El-Khamy, Jungwon Lee, and Larry Davis. Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 953--961. IEEE, 2017."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241559"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2019.8730705"},{"key":"e_1_3_2_1_15_1","unstructured":"Yue Gao Jun-Hai Yong and Fuhua Frank Cheng. Video shot boundary detection using frame-skipping technique. 20JJ-02-i8. http:\/\/www.cs.uky.edulchenglPUBLIPaper BD 1 2011.  Yue Gao Jun-Hai Yong and Fuhua Frank Cheng. Video shot boundary detection using frame-skipping technique. 20JJ-02-i8. http:\/\/www.cs.uky.edulchenglPUBLIPaper BD 1 2011."},{"key":"e_1_3_2_1_16_1","volume-title":"Lstm: A search space odyssey","author":"Greff Klaus","year":"2016","unstructured":"Klaus Greff , Rupesh K Srivastava , Jan Koutn\u00edk , Bas R Steunebrink , and J\u00fcrgen Schmidhuber . Lstm: A search space odyssey . IEEE transactions on neural networks and learning systems, 28(10):2222--2232, 2016 . Klaus Greff, Rupesh K Srivastava, Jan Koutn\u00edk, Bas R Steunebrink, and J\u00fcrgen Schmidhuber. Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10):2222--2232, 2016."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ARITH48897.2020.00029"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"e_1_3_2_1_22_1","volume-title":"Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991","author":"Huang Zhiheng","year":"2015","unstructured":"Zhiheng Huang , Wei Xu , and Kai Yu . Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991 , 2015 . Zhiheng Huang, Wei Xu, and Kai Yu. Bidirectional lstm-crf models for sequence tagging. arXiv preprint arXiv:1508.01991, 2015."},{"key":"e_1_3_2_1_23_1","volume-title":"Flexibo: Cost-aware multi-objective optimization of deep neural networks. arXiv preprint arXiv:2001.06588","author":"Iqbal Md Shahriar","year":"2020","unstructured":"Md Shahriar Iqbal , Jianhai Su , Lars Kotthoff , and Pooyan Jamshidi . Flexibo: Cost-aware multi-objective optimization of deep neural networks. arXiv preprint arXiv:2001.06588 , 2020 . Md Shahriar Iqbal, Jianhai Su, Lars Kotthoff, and Pooyan Jamshidi. Flexibo: Cost-aware multi-objective optimization of deep neural networks. arXiv preprint arXiv:2001.06588, 2020."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC.2019.8666562"},{"key":"e_1_3_2_1_25_1","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky , Geoffrey Hinton , Learning multiple layers of features from tiny images . 2009 . Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009."},{"key":"e_1_3_2_1_26_1","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . Imagenet classification with deep convolutional neural networks . In Advances in neural information processing systems , pages 1097 -- 1105 , 2012 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_28_1","volume-title":"Lenet-5, convolutional neural networks. URL: http:\/\/yann.lecun. com\/exdb\/lenet, 20(5):14","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun Lenet-5, convolutional neural networks. URL: http:\/\/yann.lecun. com\/exdb\/lenet, 20(5):14 , 2015 . Yann LeCun et al. Lenet-5, convolutional neural networks. URL: http:\/\/yann.lecun. com\/exdb\/lenet, 20(5):14, 2015."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299874.3319492"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00783"},{"key":"e_1_3_2_1_31_1","first-page":"222","volume-title":"CVPR Workshops","author":"Li Peilun","year":"2019","unstructured":"Peilun Li , Guozhen Li , Zhangxi Yan , Youzeng Li , Meiqi Lu , Pengfei Xu , Yang Gu , Bing Bai , Yifei Zhang , and DiDi Chuxing . Spatio-temporal consistency and hierarchical matching for multi-target multi-camera vehicle tracking . In CVPR Workshops , pages 222 -- 230 , 2019 . Peilun Li, Guozhen Li, Zhangxi Yan, Youzeng Li, Meiqi Lu, Pengfei Xu, Yang Gu, Bing Bai, Yifei Zhang, and DiDi Chuxing. Spatio-temporal consistency and hierarchical matching for multi-target multi-camera vehicle tracking. In CVPR Workshops, pages 222--230, 2019."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3300116"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5924"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196391"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-020-09950-3"},{"key":"e_1_3_2_1_36_1","unstructured":"NVIDIA. Cuda toolkit documentation. https:\/\/docs.nvidia.com\/cuda\/cuda-c-programming-guide\/index.html#context.  NVIDIA. Cuda toolkit documentation. https:\/\/docs.nvidia.com\/cuda\/cuda-c-programming-guide\/index.html#context."},{"key":"e_1_3_2_1_37_1","first-page":"8024","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . Pytorch : An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett, editors , Advances in Neural Information Processing Systems 32 , pages 8024 -- 8035 . Curran Associates, Inc. , 2019 . Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024--8035. Curran Associates, Inc., 2019."},{"key":"e_1_3_2_1_38_1","volume-title":"31st Euromicro Conference on Real-Time Systems (ECRTS 2019","volume":"23","author":"Pujol Roger","year":"2019","unstructured":"Roger Pujol , Hamid Tabani , Leonidas Kosmidis , Enrico Mezzetti , Jaume Abella Ferrer , and Francisco J Cazorla . Generating and exploiting deep learning variants to increase heterogeneous resource utilization in the nvidia xavier . In 31st Euromicro Conference on Real-Time Systems (ECRTS 2019 ), volume 23 , 2019 . Roger Pujol, Hamid Tabani, Leonidas Kosmidis, Enrico Mezzetti, Jaume Abella Ferrer, and Francisco J Cazorla. Generating and exploiting deep learning variants to increase heterogeneous resource utilization in the nvidia xavier. In 31st Euromicro Conference on Real-Time Systems (ECRTS 2019), volume 23, 2019."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3380891"},{"key":"e_1_3_2_1_40_1","volume-title":"Yolov3: An incremental improvement. arXiv","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi . Yolov3: An incremental improvement. arXiv , 2018 . Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv, 2018."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2655045"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2657381"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"e_1_3_2_1_44_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014 . Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/REAL.1994.342735"},{"key":"e_1_3_2_1_46_1","volume-title":"Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, (0):-","author":"Stallkamp J.","year":"2012","unstructured":"J. Stallkamp , M. Schlipsing , J. Salmen , and C. Igel . Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, (0):- , 2012 . J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, (0):-, 2012."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2857362"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2011.11"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"e_1_3_2_1_50_1","volume-title":"Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc V Le . Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 , 2019 . Mingxing Tan and Quoc V Le. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946, 2019."},{"key":"e_1_3_2_1_51_1","volume-title":"MLSys","author":"Tang Xiaohu","year":"2021","unstructured":"Xiaohu Tang , Shihao Han , Li Lyna Zhang , Ting Cao , and Yunxin Liu . To bridge neural network design and real-world performance: A behaviour study for neural networks . In MLSys , April 2021 . Xiaohu Tang, Shihao Han, Li Lyna Zhang, Ting Cao, and Yunxin Liu. To bridge neural network design and real-world performance: A behaviour study for neural networks. In MLSys, April 2021."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448625"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS46320.2019.00042"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.5555\/3196158.3196225"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3300061.3345448"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS.2018.00018"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS.2019.00033"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274783.3274840"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131675"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1900120"},{"key":"e_1_3_2_1_62_1","first-page":"1","volume-title":"Proceedings of the 26th Annual International Conference on Mobile Computing and Networking","author":"Yi Juheon","year":"2020","unstructured":"Juheon Yi and Youngki Lee . Heimdall : mobile gpu coordination platform for augmented reality applications . In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking , pages 1 -- 14 , 2020 . Juheon Yi and Youngki Lee. Heimdall: mobile gpu coordination platform for augmented reality applications. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, pages 1--14, 2020."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.3390\/fi11040094"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMULT.2010.5630326"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/RTAS.2018.00028"}],"event":{"name":"SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems","location":"Coimbra Portugal","acronym":"SenSys '21","sponsor":["SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGCOMM ACM Special Interest Group on Data Communication","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems","SIGBED ACM Special Interest Group on Embedded Systems","SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485730.3485938","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485730.3485938","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:10Z","timestamp":1750191130000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485730.3485938"}},"subtitle":["Supporting Real-Time Mixed Deep Learning Tasks on Edge Platforms"],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":65,"alternative-id":["10.1145\/3485730.3485938","10.1145\/3485730"],"URL":"https:\/\/doi.org\/10.1145\/3485730.3485938","relation":{},"subject":[],"published":{"date-parts":[[2021,11,15]]},"assertion":[{"value":"2021-11-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}