{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:43:08Z","timestamp":1776836588417,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,10,11]],"date-time":"2018-10-11T00:00:00Z","timestamp":1539216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Research Foundation of Korea","award":["NRF-2017R1A2B2005562"],"award-info":[{"award-number":["NRF-2017R1A2B2005562"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,10,11]]},"DOI":"10.1145\/3267809.3267828","type":"proceedings-article","created":{"date-parts":[[2018,9,28]],"date-time":"2018-09-28T18:00:41Z","timestamp":1538157641000},"page":"401-411","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":129,"title":["IONN"],"prefix":"10.1145","author":[{"given":"Hyuk-Jin","family":"Jeong","sequence":"first","affiliation":[{"name":"Seoul National University, Seoul, South Korea"}]},{"given":"Hyeon-Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University, Seoul, South Korea"}]},{"given":"Chang Hyun","family":"Shin","sequence":"additional","affiliation":[{"name":"Seoul National University, Seoul, South Korea"}]},{"given":"Soo-Mook","family":"Moon","sequence":"additional","affiliation":[{"name":"Seoul National University, Seoul, South Korea"}]}],"member":"320","published-online":{"date-parts":[[2018,10,11]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"9","article-title":"Compilers, Principles","volume":"7","author":"Aho Alfred V","year":"1986","journal-title":"Techniques. Addison Wesley"},{"key":"e_1_3_2_1_2_1","volume-title":"Retrieved","author":"Alchemy","year":"2009"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2342509.2342513"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1966445.1966473"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1814433.1814441"},{"key":"e_1_3_2_1_7_1","unstructured":"Pengfei Dou Shishir K. Shah and Ioannis A. Kakadiaris. 2017. End-to-end 3D face reconstruction with deep neural networks. CoRR abs\/1704.05020 (2017). arXiv:1704.05020 http:\/\/arxiv.org\/abs\/1704.05020  Pengfei Dou Shishir K. Shah and Ioannis A. Kakadiaris. 2017. End-to-end 3D face reconstruction with deep neural networks. CoRR abs\/1704.05020 (2017). arXiv:1704.05020 http:\/\/arxiv.org\/abs\/1704.05020"},{"key":"e_1_3_2_1_8_1","unstructured":"Mart\u00edn Abadi et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs\/1603.04467 (2016). arXiv: 1603.04467 http:\/\/arxiv.org\/abs\/1603.04467  Mart\u00edn Abadi et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs\/1603.04467 (2016). arXiv: 1603.04467 http:\/\/arxiv.org\/abs\/1603.04467"},{"key":"e_1_3_2_1_9_1","volume-title":"Retrieved","author":"Goodrich Michael T.","year":"1996"},{"key":"e_1_3_2_1_10_1","volume-title":"Retrieved","year":"2008"},{"key":"e_1_3_2_1_11_1","volume-title":"Acoustics, speech and signal processing (icassp)","author":"Graves Alex","year":"2013"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594368.2594383"},{"key":"e_1_3_2_1_13_1","unstructured":"Song Han Huizi Mao and William J. Dally. 2015. Deep Compression: Compressing Deep Neural Network with Pruning Trained Quantization and Huffman Coding. CoRR abs\/1510.00149 (2015). arXiv:1510.00149 http:\/\/arxiv.org\/abs\/1510.00149  Song Han Huizi Mao and William J. Dally. 2015. Deep Compression: Compressing Deep Neural Network with Pruning Trained Quantization and Huffman Coding. CoRR abs\/1510.00149 (2015). arXiv:1510.00149 http:\/\/arxiv.org\/abs\/1510.00149"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_16_1","unstructured":"Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861 (2017). arXiv:1704.04861 http:\/\/arxiv.org\/abs\/1704.04861  Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs\/1704.04861 (2017). arXiv:1704.04861 http:\/\/arxiv.org\/abs\/1704.04861"},{"key":"e_1_3_2_1_17_1","unstructured":"Jie Hu Li Shen and Gang Sun. 2017. Squeeze-and-Excitation Networks. CoRR abs\/1709.01507 (2017). arXiv:1709.01507 http:\/\/arxiv.org\/abs\/1709.01507  Jie Hu Li Shen and Gang Sun. 2017. Squeeze-and-Excitation Networks. CoRR abs\/1709.01507 (2017). arXiv:1709.01507 http:\/\/arxiv.org\/abs\/1709.01507"},{"key":"e_1_3_2_1_18_1","unstructured":"Forrest N. Iandola Matthew W. Moskewicz Khalid Ashraf Song Han William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR abs\/1602.07360 (2016). arXiv:1602.07360 http:\/\/arxiv.org\/abs\/1602.07360  Forrest N. Iandola Matthew W. Moskewicz Khalid Ashraf Song Han William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR abs\/1602.07360 (2016). arXiv:1602.07360 http:\/\/arxiv.org\/abs\/1602.07360"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3037697.3037698"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2012.6195845"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems -","volume":"1","author":"Krizhevsky Alex"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2017.1700011"},{"key":"e_1_3_2_1_24_1","volume-title":"EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding. CoRR abs\/1507.08240","author":"Miao Yajie","year":"2015"},{"key":"e_1_3_2_1_25_1","volume-title":"Retrieved","author":"NVIDIA.","year":"2008"},{"key":"e_1_3_2_1_26_1","volume-title":"Retrieved","author":"Packard Hewlett","year":"2013"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/FiCloud.2015.35"},{"key":"e_1_3_2_1_28_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_30_1","volume-title":"Retrieved","author":"Roy Rob","year":"2017"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Mahadev Satyanarayanan. 2001. Pervasive computing: Vision and challenges. IEEE Personal communications 8 4 (2001) 10--17.  Mahadev Satyanarayanan. 2001. Pervasive computing: Vision and challenges. IEEE Personal communications 8 4 (2001) 10--17.","DOI":"10.1109\/98.943998"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2017.9"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2009.82"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"David Silver Aja Huang Chris J Maddison Arthur Guez Laurent Sifre George Van Den Driessche Julian Schrittwieser Ioannis Antonoglou Veda Panneershelvam Marc Lanctot et al. 2016. Mastering the game of Go with deep neural networks and tree search nature 529 7587 (2016) 484--489.  David Silver Aja Huang Chris J Maddison Arthur Guez Laurent Sifre George Van Den Driessche Julian Schrittwieser Ioannis Antonoglou Veda Panneershelvam Marc Lanctot et al. 2016. Mastering the game of Go with deep neural networks and tree search nature 529 7587 (2016) 484--489.","DOI":"10.1038\/nature16961"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems -","volume":"2","author":"Sutskever Ilya"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke Andrew Rabinovich etal 2015. Going deeper with convolutions. Cvpr.  Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke Andrew Rabinovich et al. 2015. Going deeper with convolutions. Cvpr.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_37_1","unstructured":"Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbigniew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. CoRR abs\/1512.00567 (2015). arXiv:1512.00567 http:\/\/arxiv.org\/abs\/1512.00567  Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens and Zbigniew Wojna. 2015. Rethinking the Inception Architecture for Computer Vision. CoRR abs\/1512.00567 (2015). arXiv:1512.00567 http:\/\/arxiv.org\/abs\/1512.00567"},{"key":"e_1_3_2_1_38_1","unstructured":"Anh Tuan Tran Tal Hassner Iacopo Masi and G\u00e9rard G. Medioni. 2016. Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network. CoRR abs\/1612.04904 (2016). arXiv:1612.04904 http:\/\/arxiv.org\/abs\/1612.04904  Anh Tuan Tran Tal Hassner Iacopo Masi and G\u00e9rard G. Medioni. 2016. Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network. CoRR abs\/1612.04904 (2016). arXiv:1612.04904 http:\/\/arxiv.org\/abs\/1612.04904"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2014.2366735"}],"event":{"name":"SoCC '18: ACM Symposium on Cloud Computing","location":"Carlsbad CA USA","acronym":"SoCC '18","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the ACM Symposium on Cloud Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3267809.3267828","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3267809.3267828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:44:30Z","timestamp":1750207470000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3267809.3267828"}},"subtitle":["Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers"],"short-title":[],"issued":{"date-parts":[[2018,10,11]]},"references-count":39,"alternative-id":["10.1145\/3267809.3267828","10.1145\/3267809"],"URL":"https:\/\/doi.org\/10.1145\/3267809.3267828","relation":{},"subject":[],"published":{"date-parts":[[2018,10,11]]},"assertion":[{"value":"2018-10-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}