{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:05:25Z","timestamp":1775815525800,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":73,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,18]]},"DOI":"10.1145\/3581791.3596842","type":"proceedings-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T17:52:21Z","timestamp":1686937941000},"page":"516-529","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Boosting DNN Cold Inference on Edge Devices"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6040-9596","authenticated-orcid":false,"given":"Rongjie","family":"Yi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9107-013X","authenticated-orcid":false,"given":"Ting","family":"Cao","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5743-9418","authenticated-orcid":false,"given":"Ao","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5742-8890","authenticated-orcid":false,"given":"Xiao","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7245-1298","authenticated-orcid":false,"given":"Shangguang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6271-6993","authenticated-orcid":false,"given":"Mengwei","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"https:\/\/play.google.com\/store\/apps\/details?id=com.adobe.creativeapps.gather&gl=US","author":"Adobe","year":"2022","unstructured":"Adobe capture. https:\/\/play.google.com\/store\/apps\/details?id=com.adobe.creativeapps.gather&gl=US , 2022 . Adobe capture. https:\/\/play.google.com\/store\/apps\/details?id=com.adobe.creativeapps.gather&gl=US, 2022."},{"key":"e_1_3_2_1_2_1","volume-title":"https:\/\/support.google.com\/googlenest\/answer\/9136909?hl=en","author":"Introducing","year":"2022","unstructured":"Introducing google nest hub. https:\/\/support.google.com\/googlenest\/answer\/9136909?hl=en , 2022 . Introducing google nest hub. https:\/\/support.google.com\/googlenest\/answer\/9136909?hl=en, 2022."},{"key":"e_1_3_2_1_3_1","volume-title":"https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-tx2\/","author":"Jetson","year":"2022","unstructured":"Jetson tx2 modules. https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-tx2\/ , 2022 . Jetson tx2 modules. https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-tx2\/, 2022."},{"key":"e_1_3_2_1_4_1","volume-title":"https:\/\/play.google.com\/store\/apps\/details?id=com.camscanner.documentscanner.pdfscanner.textscanner.photos.scanner&gl=US","author":"Pdf","year":"2022","unstructured":"Pdf scanner - document scanner. https:\/\/play.google.com\/store\/apps\/details?id=com.camscanner.documentscanner.pdfscanner.textscanner.photos.scanner&gl=US , 2022 . Pdf scanner - document scanner. https:\/\/play.google.com\/store\/apps\/details?id=com.camscanner.documentscanner.pdfscanner.textscanner.photos.scanner&gl=US, 2022."},{"key":"e_1_3_2_1_5_1","volume-title":"- all picture art. https:\/\/play.google.com\/store\/apps\/details?id=com.supe.photoeditor","author":"Photo","year":"2022","unstructured":"Photo editor - all picture art. https:\/\/play.google.com\/store\/apps\/details?id=com.supe.photoeditor &gl=US, 2022 . Photo editor- all picture art. https:\/\/play.google.com\/store\/apps\/details?id=com.supe.photoeditor&gl=US, 2022."},{"key":"e_1_3_2_1_6_1","volume-title":"https:\/\/en.wikipedia.org\/wiki\/Shader","author":"Shader","year":"2022","unstructured":"Shader - wikipedia. https:\/\/en.wikipedia.org\/wiki\/Shader , 2022 . Shader - wikipedia. https:\/\/en.wikipedia.org\/wiki\/Shader, 2022."},{"key":"e_1_3_2_1_7_1","volume-title":"https:\/\/www.qualcomm.com\/products\/snapdragon-765g-5g-mobile-platform","author":"Snapdragon","year":"2022","unstructured":"Snapdragon 765g 5g mobile platform. https:\/\/www.qualcomm.com\/products\/snapdragon-765g-5g-mobile-platform , 2022 . Snapdragon 765g 5g mobile platform. https:\/\/www.qualcomm.com\/products\/snapdragon-765g-5g-mobile-platform, 2022."},{"key":"e_1_3_2_1_8_1","volume-title":"https:\/\/github.com\/Tencent\/ncnn","author":"Tencent","year":"2022","unstructured":"Tencent ncnn. https:\/\/github.com\/Tencent\/ncnn , 2022 . Tencent ncnn. https:\/\/github.com\/Tencent\/ncnn, 2022."},{"key":"e_1_3_2_1_9_1","volume-title":"https:\/\/github.com\/Tencent\/ncnn\/tree\/master\/benchmark","author":"Tencent","year":"2022","unstructured":"Tencent ncnn benchmark. https:\/\/github.com\/Tencent\/ncnn\/tree\/master\/benchmark , 2022 . Tencent ncnn benchmark. https:\/\/github.com\/Tencent\/ncnn\/tree\/master\/benchmark, 2022."},{"key":"e_1_3_2_1_10_1","volume-title":"https:\/\/www.tensorflow.org\/lite\/","author":"Tensorflow","year":"2022","unstructured":"Tensorflow lite. https:\/\/www.tensorflow.org\/lite\/ , 2022 . Tensorflow lite. https:\/\/www.tensorflow.org\/lite\/, 2022."},{"key":"e_1_3_2_1_11_1","volume-title":"https:\/\/www.khronos.org\/registry\/vulkan\/specs\/1.3-extensions\/man\/html\/VkPipeline.html","author":"Vkpipeline","year":"2022","unstructured":"Vkpipeline - opaque handle to a pipeline object. https:\/\/www.khronos.org\/registry\/vulkan\/specs\/1.3-extensions\/man\/html\/VkPipeline.html , 2022 . Vkpipeline - opaque handle to a pipeline object. https:\/\/www.khronos.org\/registry\/vulkan\/specs\/1.3-extensions\/man\/html\/VkPipeline.html, 2022."},{"key":"e_1_3_2_1_12_1","volume-title":"https:\/\/vulkan-tutorial.com\/Overview#page_Step-6-Graphics-pipeline","author":"Vulkan","year":"2022","unstructured":"Vulkan tutorial - graphics pipeline. https:\/\/vulkan-tutorial.com\/Overview#page_Step-6-Graphics-pipeline , 2022 . Vulkan tutorial - graphics pipeline. https:\/\/vulkan-tutorial.com\/Overview#page_Step-6-Graphics-pipeline, 2022."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487552.3487863"},{"key":"e_1_3_2_1_14_1","volume-title":"Neural network on compute shader: Running and training a neural network using gpgpu","author":"\u00c5str\u00f6m Fredrik","year":"2011","unstructured":"Fredrik \u00c5str\u00f6m . Neural network on compute shader: Running and training a neural network using gpgpu , 2011 . Fredrik \u00c5str\u00f6m. Neural network on compute shader: Running and training a neural network using gpgpu, 2011."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685302"},{"key":"e_1_3_2_1_16_1","first-page":"499","volume-title":"14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)","author":"Bai Zhihao","year":"2020","unstructured":"Zhihao Bai , Zhen Zhang , Yibo Zhu , and Xin Jin . {PipeSwitch} : Fast pipelined context switching for deep learning applications . In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) , pages 499 -- 514 , 2020 . Zhihao Bai, Zhen Zhang, Yibo Zhu, and Xin Jin. {PipeSwitch}: Fast pipelined context switching for deep learning applications. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pages 499--514, 2020."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994564"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2011.5980058"},{"key":"e_1_3_2_1_19_1","first-page":"406","article-title":"Scaling video analytics on constrained edge nodes","volume":"1","author":"Canel Christopher","year":"2019","unstructured":"Christopher Canel , Thomas Kim , Giulio Zhou , Conglong Li , Hyeontaek Lim , David G Andersen , Michael Kaminsky , and Subramanya Dulloor . Scaling video analytics on constrained edge nodes . Proceedings of Machine Learning and Systems , 1 : 406 -- 417 , 2019 . Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G Andersen, Michael Kaminsky, and Subramanya Dulloor. Scaling video analytics on constrained edge nodes. Proceedings of Machine Learning and Systems, 1:406--417, 2019.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3089801.3089804"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241559"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTCOMP.2017.7946996"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2973750.2973777"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_27_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 , 2017 . Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081360"},{"key":"e_1_3_2_1_29_1","volume-title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360","author":"Iandola Forrest N","year":"2016","unstructured":"Forrest N Iandola , Song Han , Matthew W Moskewicz , Khalid Ashraf , William J Dally , and Kurt Keutzer . Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360 , 2016 . Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360, 2016."},{"key":"e_1_3_2_1_30_1","volume-title":"et al. Mnn: A universal and efficient inference engine. arXiv preprint arXiv:2002.12418","author":"Jiang Xiaotang","year":"2020","unstructured":"Xiaotang Jiang , Huan Wang , Yiliu Chen , Ziqi Wu , Lichuan Wang , Bin Zou , Yafeng Yang , Zongyang Cui , Yu Cai , Tianhang Yu , et al. Mnn: A universal and efficient inference engine. arXiv preprint arXiv:2002.12418 , 2020 . Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, et al. Mnn: A universal and efficient inference engine. arXiv preprint arXiv:2002.12418, 2020."},{"key":"e_1_3_2_1_31_1","volume-title":"9th USENIX Conference on File and Storage Technologies (FAST 11)","author":"Joo Yongsoo","year":"2011","unstructured":"Yongsoo Joo , Junhee Ryu , Sangsoo Park , and Kang G Shin . {FAST} : Quick application launch on {Solid-State} drives . In 9th USENIX Conference on File and Storage Technologies (FAST 11) , 2011 . Yongsoo Joo, Junhee Ryu, Sangsoo Park, and Kang G Shin. {FAST}: Quick application launch on {Solid-State} drives. In 9th USENIX Conference on File and Storage Technologies (FAST 11), 2011."},{"key":"e_1_3_2_1_32_1","first-page":"1","volume-title":"Proceedings of the Fourteenth EuroSys Conference 2019","author":"Kim Youngsok","year":"2019","unstructured":"Youngsok Kim , Joonsung Kim , Dongju Chae , Daehyun Kim , and Jangwoo Kim . \u03bclayer : Low latency on-device inference using cooperative single-layer acceleration and processor-friendly quantization . In Proceedings of the Fourteenth EuroSys Conference 2019 , pages 1 -- 15 , 2019 . Youngsok Kim, Joonsung Kim, Dongju Chae, Daehyun Kim, and Jangwoo Kim. \u03bclayer: Low latency on-device inference using cooperative single-layer acceleration and processor-friendly quantization. In Proceedings of the Fourteenth EuroSys Conference 2019, pages 1--15, 2019."},{"key":"e_1_3_2_1_33_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25 , 2012 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460664"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419194"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.435"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971680"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386901.3388947"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2014.78"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517020"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210337"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313639"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/2813767.2813810"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081359"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378534"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTW.2019.00038"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2493432.2493490"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341617.3326142"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_51_1","volume-title":"Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi . Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 , 2018 . Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_54_1","first-page":"6105","volume-title":"International conference on machine learning","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. Efficientnet : Rethinking model scaling for convolutional neural networks . In International conference on machine learning , pages 6105 -- 6114 . PMLR, 2019 . Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105--6114. PMLR, 2019."},{"key":"e_1_3_2_1_55_1","volume-title":"Deep learning for computer vision: A brief review. Computational intelligence and neuroscience","author":"Voulodimos Athanasios","year":"2018","unstructured":"Athanasios Voulodimos , Nikolaos Doulamis , Anastasios Doulamis , and Eftychios Protopapadakis . Deep learning for computer vision: A brief review. Computational intelligence and neuroscience , 2018 , 2018. Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 2018, 2018."},{"key":"e_1_3_2_1_56_1","first-page":"215","volume-title":"MobiCom","author":"Wang Manni","year":"2021","unstructured":"Manni Wang , Shaohua Ding , Ting Cao , Yunxin Liu , and Fengyuan Xu. Asymo : scalable and efficient deep-learning inference on asymmetric mobile cpus . In MobiCom , pages 215 -- 228 , 2021 . Manni Wang, Shaohua Ding, Ting Cao, Yunxin Liu, and Fengyuan Xu. Asymo: scalable and efficient deep-learning inference on asymmetric mobile cpus. In MobiCom, pages 215--228, 2021."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538928"},{"key":"e_1_3_2_1_58_1","first-page":"473","volume-title":"Demystifying page load performance with {WProf}.In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13)","author":"Wang Xiao Sophia","year":"2013","unstructured":"Xiao Sophia Wang , Aruna Balasubramanian , Arvind Krishnamurthy , and David Wetherall . Demystifying page load performance with {WProf}.In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13) , pages 473 -- 485 , 2013 . Xiao Sophia Wang, Aruna Balasubramanian, Arvind Krishnamurthy, and David Wetherall. Demystifying page load performance with {WProf}.In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), pages 473--485, 2013."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448618"},{"key":"e_1_3_2_1_60_1","volume-title":"Towards monocular vision based obstacle avoidance through deep reinforcement learning. arXiv preprint arXiv:1706.09829","author":"Xie Linhai","year":"2017","unstructured":"Linhai Xie , Sen Wang , Andrew Markham , and Niki Trigoni . Towards monocular vision based obstacle avoidance through deep reinforcement learning. arXiv preprint arXiv:1706.09829 , 2017 . Linhai Xie, Sen Wang, Andrew Markham, and Niki Trigoni. Towards monocular vision based obstacle avoidance through deep reinforcement learning. arXiv preprint arXiv:1706.09829, 2017."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3560545"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487552.3487815"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313591"},{"issue":"4","key":"e_1_3_2_1_64_1","first-page":"1","article-title":"On-device deep learning for input personalization service with minimal privacy concern","volume":"2","author":"Xu Mengwei","year":"2018","unstructured":"Mengwei Xu , Feng Qian , Qiaozhu Mei , Kang Huang , and Xuanzhe Liu . Deeptype : On-device deep learning for input personalization service with minimal privacy concern . IMWUT , 2 ( 4 ): 1 -- 26 , 2018 . Mengwei Xu, Feng Qian, Qiaozhu Mei, Kang Huang, and Xuanzhe Liu. Deeptype: On-device deep learning for input personalization service with minimal privacy concern. IMWUT, 2(4):1--26, 2018.","journal-title":"IMWUT"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2893250"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386901.3388948"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241563"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2307636.2307648"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449851"},{"key":"e_1_3_2_1_70_1","volume-title":"Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13(3):55--75","author":"Young Tom","year":"2018","unstructured":"Tom Young , Devamanyu Hazarika , Soujanya Poria , and Erik Cambria . Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13(3):55--75 , 2018 . Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13(3):55--75, 2018."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD51958.2021.9643501"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467882"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"}],"event":{"name":"MobiSys '23: 21st Annual International Conference on Mobile Systems, Applications and Services","location":"Helsinki Finland","acronym":"MobiSys '23","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581791.3596842","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:30Z","timestamp":1750178190000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581791.3596842"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":73,"alternative-id":["10.1145\/3581791.3596842","10.1145\/3581791"],"URL":"https:\/\/doi.org\/10.1145\/3581791.3596842","relation":{},"subject":[],"published":{"date-parts":[[2023,6,18]]},"assertion":[{"value":"2023-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}