{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:37:06Z","timestamp":1773218226956,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T00:00:00Z","timestamp":1560816000000},"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":[[2019,6,18]]},"DOI":"10.1145\/3304109.3306221","type":"proceedings-article","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T12:14:30Z","timestamp":1560860070000},"page":"36-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Serving deep neural networks at the cloud edge for vision applications on mobile platforms"],"prefix":"10.1145","author":[{"given":"Zhou","family":"Fang","sequence":"first","affiliation":[{"name":"University of California, San Diego"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dezhi","family":"Hong","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajesh K.","family":"Gupta","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,6,18]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"AWS Machine Learning. https:\/\/aws.amazon.com\/machine-learning\/. {Online; accessed 16-July-2018}.  AWS Machine Learning. https:\/\/aws.amazon.com\/machine-learning\/. {Online; accessed 16-July-2018}."},{"key":"e_1_3_2_1_2_1","unstructured":"Azure Machine Learning. https:\/\/azure.microsoft.com\/overview\/machine-learning\/. {Online; accessed 16-July-2018}.  Azure Machine Learning. https:\/\/azure.microsoft.com\/overview\/machine-learning\/. {Online; accessed 16-July-2018}."},{"key":"e_1_3_2_1_3_1","unstructured":"Google Cloud Machine Learning. https:\/\/cloud.google.com\/products\/machine-learning\/. {Online; accessed 16-July-2018}.  Google Cloud Machine Learning. https:\/\/cloud.google.com\/products\/machine-learning\/. {Online; accessed 16-July-2018}."},{"key":"e_1_3_2_1_4_1","unstructured":"Tensorflow Detection Model Zoo. https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/object_detection\/g3doc\/detection_model_zoo.md. {Online; accessed 16-July-2018}.  Tensorflow Detection Model Zoo. https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/object_detection\/g3doc\/detection_model_zoo.md. {Online; accessed 16-July-2018}."},{"key":"e_1_3_2_1_5_1","unstructured":"The AWS DeepLens. https:\/\/aws.amazon.com\/deeplens\/. {Online; accessed 16-July-2018}.  The AWS DeepLens. https:\/\/aws.amazon.com\/deeplens\/. {Online; accessed 16-July-2018}."},{"key":"e_1_3_2_1_6_1","unstructured":"The Google Glasses. https:\/\/x.company\/glass\/. {Online; accessed 16-July-2018}.  The Google Glasses. https:\/\/x.company\/glass\/. {Online; accessed 16-July-2018}."},{"key":"e_1_3_2_1_7_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In OSDI","year":"2016"},{"key":"e_1_3_2_1_8_1","volume-title":"HOTCHIPS","author":"Ajayi T.","year":"2017"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"e_1_3_2_1_10_1","volume-title":"Fully-Convolutional Siamese Networks for Object Tracking. In ECCV Workshops","author":"Bertinetto L.","year":"2016"},{"key":"e_1_3_2_1_11_1","volume-title":"Real-Time Object Recognition on Mobile Devices. In SenSys","year":"2015"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132211.3134458"},{"key":"e_1_3_2_1_13_1","volume-title":"NSDI","author":"Crankshaw D.","year":"2017"},{"key":"e_1_3_2_1_14_1","first-page":"751","volume-title":"ECCV","author":"A","year":"2000"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00012"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594368.2594383"},{"key":"e_1_3_2_1_17_1","unstructured":"S. Han etal Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv:1510.00149 2015.  S. Han et al. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv:1510.00149 2015."},{"key":"e_1_3_2_1_18_1","volume-title":"MobiSys","year":"2016"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.30"},{"key":"e_1_3_2_1_20_1","unstructured":"W. Han etal Seq-NMS for Video Object Detection. arXiv:1602.08465 2016.  W. Han et al. Seq-NMS for Video Object Detection. arXiv:1602.08465 2016."},{"key":"e_1_3_2_1_21_1","volume-title":"MLHC","author":"Haque A.","year":"2017"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2749472"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2694344.2694347"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_25_1","volume-title":"ICCV","author":"He K.","year":"2017"},{"key":"e_1_3_2_1_26_1","volume-title":"CVPR","year":"2015"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_28_1","unstructured":"Andrew G. Howard et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.1704.04861 2017.  Andrew G. Howard et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.1704.04861 2017."},{"key":"e_1_3_2_1_29_1","volume-title":"SEC","author":"Hung C.-C.","year":"2018"},{"key":"e_1_3_2_1_30_1","unstructured":"F. N. Iandola etal SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv.1602.07360 2016.  F. N. Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv.1602.07360 2016."},{"key":"e_1_3_2_1_31_1","volume-title":"ISCA","author":"P.","year":"2017"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3037697.3037698"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0981-7"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800020109"},{"key":"e_1_3_2_1_35_1","volume-title":"ECCV","author":"Lin T.-Y.","year":"2014"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987564"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2694344.2694346"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847265"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1068\/p6794"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2009.82"},{"key":"e_1_3_2_1_42_1","volume-title":"ICLR","author":"Simonyan K.","year":"2015"},{"key":"e_1_3_2_1_43_1","volume-title":"ISCA","author":"Tanasic I.","year":"2014"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.515"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.60"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.521"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.571"},{"key":"e_1_3_2_1_49_1","unstructured":"T. Yao etal MSR A MSM at ActivityNet challenge 2017. http:\/\/home.ustc.edu.cn\/~panywei\/paper\/Activitynet17.pdf 2017. {Online}.  T. Yao et al. MSR A MSM at ActivityNet challenge 2017. http:\/\/home.ustc.edu.cn\/~panywei\/paper\/Activitynet17.pdf 2017. {Online}."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684746.2689060"},{"key":"e_1_3_2_1_51_1","volume-title":"NSDI","author":"Zhang H.","year":"2017"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790123"},{"key":"e_1_3_2_1_53_1","unstructured":"B. Zoph etal Learning Transferable Architectures for Scalable Image Recognition. arXiv 1707.07012 2017.  B. Zoph et al. Learning Transferable Architectures for Scalable Image Recognition. arXiv 1707.07012 2017."}],"event":{"name":"MMSys '19: 10th ACM Multimedia Systems Conference","location":"Amherst Massachusetts","acronym":"MMSys '19","sponsor":["SIGMM ACM Special Interest Group on Multimedia","SIGCOMM ACM Special Interest Group on Data Communication","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"]},"container-title":["Proceedings of the 10th ACM Multimedia Systems Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3304109.3306221","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3304109.3306221","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:19Z","timestamp":1750208539000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3304109.3306221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,18]]},"references-count":53,"alternative-id":["10.1145\/3304109.3306221","10.1145\/3304109"],"URL":"https:\/\/doi.org\/10.1145\/3304109.3306221","relation":{},"subject":[],"published":{"date-parts":[[2019,6,18]]},"assertion":[{"value":"2019-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}