{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:29:58Z","timestamp":1750220998469,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T00:00:00Z","timestamp":1564272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CSR 1815047"],"award-info":[{"award-number":["CSR 1815047"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,28]]},"DOI":"10.1145\/3332186.3333049","type":"proceedings-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T19:55:41Z","timestamp":1564516541000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Characterizing the Execution of Deep Neural Networks on Collaborative Robots and Edge Devices"],"prefix":"10.1145","author":[{"given":"Matthew L.","family":"Merck","sequence":"first","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyao","family":"Wang","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixing","family":"Liu","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunjun","family":"Jia","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arthur","family":"Siqueira","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiusen","family":"Huang","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhijeet","family":"Saraha","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsuk","family":"Lim","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiashen","family":"Cao","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramyad","family":"Hadidi","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyesoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Georgia Tech"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,7,28]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi et al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org.  Mart\u00edn Abadi et al. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow.org."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3322472"},{"key":"e_1_3_2_1_3_1","volume-title":"ICLR'15)","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural Machine Translation by Jointly Learning to Align and Translate . In ICLR'15) . ACM. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In ICLR'15). ACM."},{"key":"e_1_3_2_1_4_1","unstructured":"F Biscotti J Skorupa R Contu etal 2014. The Impact of the Internet of Things on Data Centers. Gartner Research 18 (2014).  F Biscotti J Skorupa R Contu et al. 2014. The Impact of the Internet of Things on Data Centers. Gartner Research 18 (2014)."},{"key":"e_1_3_2_1_5_1","unstructured":"Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/github.com\/fchollet\/keras.  Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/github.com\/fchollet\/keras."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_3_2_1_7_1","volume-title":"Training Deep Neural Networks with Low Precision Multiplication. arXiv preprint arXiv:1412.7024","author":"Courbariaux Matthieu","year":"2014","unstructured":"Matthieu Courbariaux , Yoshua Bengio , and Jean-Pierre David . 2014. Training Deep Neural Networks with Low Precision Multiplication. arXiv preprint arXiv:1412.7024 ( 2014 ). Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. 2014. Training Deep Neural Networks with Low Precision Multiplication. arXiv preprint arXiv:1412.7024 (2014)."},{"key":"e_1_3_2_1_8_1","unstructured":"Raspberry PI Foundation. 2017. Raspberry Pi 3B+. www.raspber-rypi.org\/products\/raspberry-pi-3-model-b\/. {Online; accessed 04\/01\/19}.  Raspberry PI Foundation. 2017. Raspberry Pi 3B+. www.raspber-rypi.org\/products\/raspberry-pi-3-model-b\/. {Online; accessed 04\/01\/19}."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2015.2509024"},{"key":"e_1_3_2_1_10_1","volume-title":"Compressing Deep Convolutional Networks Using Vector Quantization. arXiv preprint arXiv:1412.6115","author":"Gong Yunchao","year":"2014","unstructured":"Yunchao Gong , Liu Liu , Ming Yang , and Lubomir Bourdev . 2014. Compressing Deep Convolutional Networks Using Vector Quantization. arXiv preprint arXiv:1412.6115 ( 2014 ). Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev. 2014. Compressing Deep Convolutional Networks Using Vector Quantization. arXiv preprint arXiv:1412.6115 (2014)."},{"key":"e_1_3_2_1_11_1","first-page":"595","article-title":"Discovering cloud-based services for iot devices in an iot network associated with a user","volume":"14","author":"Gupta Binita","year":"2015","unstructured":"Binita Gupta . 2015 . Discovering cloud-based services for iot devices in an iot network associated with a user . US Patent App. 14\/550 , 595 . Binita Gupta. 2015. Discovering cloud-based services for iot devices in an iot network associated with a user. US Patent App. 14\/550,595.","journal-title":"US Patent App."},{"key":"e_1_3_2_1_12_1","volume-title":"Collaborative Execution of Deep Neural Networks on Internet of Things Device. arXiv preprint","author":"Hadidi Ramyad","year":"2018","unstructured":"Ramyad Hadidi , Jiashen Cao , Michael Ryoo , and Hyesoon Kim . 2018. Collaborative Execution of Deep Neural Networks on Internet of Things Device. arXiv preprint ( 2018 ). Ramyad Hadidi, Jiashen Cao, Michael Ryoo, and Hyesoon Kim. 2018. Collaborative Execution of Deep Neural Networks on Internet of Things Device. arXiv preprint (2018)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2856261"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3322474"},{"key":"e_1_3_2_1_15_1","volume-title":"Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices. arXiv preprint arXiv:1802.02138","author":"Hadidi Ramyad","year":"2018","unstructured":"Ramyad Hadidi , Jiashen Cao , Matthew Woodward , Michael Ryoo , and Hyesoon Kim . 2018 . Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices. arXiv preprint arXiv:1802.02138 (2018). Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael Ryoo, and Hyesoon Kim. 2018. Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices. arXiv preprint arXiv:1802.02138 (2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229762.3229765"},{"key":"e_1_3_2_1_17_1","volume-title":"An Edge-Centric Scalable Intelligent Framework To Collaboratively Execute DNN. Demo for SysML Conference, Palo Alto, CA","author":"Hadidi Ramyad","year":"2019","unstructured":"Ramyad Hadidi , Jiashen Cao , Fei Wu , Tushar Kirshna , Michael S. Ryoo , and Hyesoon Kim . 2019 . An Edge-Centric Scalable Intelligent Framework To Collaboratively Execute DNN. Demo for SysML Conference, Palo Alto, CA (2019). Ramyad Hadidi, Jiashen Cao, Fei Wu, Tushar Kirshna, Michael S. Ryoo, and Hyesoon Kim. 2019. An Edge-Centric Scalable Intelligent Framework To Collaboratively Execute DNN. Demo for SysML Conference, Palo Alto, CA (2019)."},{"key":"e_1_3_2_1_18_1","volume-title":"Trained Quantization and Huffman Coding. In 4th International Conference on Learning Representations. ACM.","author":"Han Song","year":"2016","unstructured":"Song Han , Huizi Mao , and William J Dally . 2016 . Deep Compression: Compressing Deep Neural Network with Pruning , Trained Quantization and Huffman Coding. In 4th International Conference on Learning Representations. ACM. Song Han, Huizi Mao, and William J Dally. 2016. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. In 4th International Conference on Learning Representations. ACM."},{"key":"e_1_3_2_1_19_1","unstructured":"iRobot Inc. 2019. iRobot Create 2 Open Interface. www.cdn-shop.adafruit.com\/datasheetscreate_2_Open_Interface_Spec.pdf. {Online; accessed 15\/03\/19}.  iRobot Inc. 2019. iRobot Create 2 Open Interface. www.cdn-shop.adafruit.com\/datasheetscreate_2_Open_Interface_Spec.pdf. {Online; accessed 15\/03\/19}."},{"key":"e_1_3_2_1_20_1","unstructured":"iRobot Inc. 2019. iRobot Create 2 Programmable Robot. www.irobot.com\/about-irobot\/stem\/create-2. {Online; accessed 15\/03\/19}.  iRobot Inc. 2019. iRobot Create 2 Programmable Robot. www.irobot.com\/about-irobot\/stem\/create-2. {Online; accessed 15\/03\/19}."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/FIT.2012.53"},{"key":"e_1_3_2_1_22_1","volume-title":"Flexpoint: An adaptive numerical format for efficient training of deep neural networks. In Advances in Neural Information Processing Systems (NIPS). 1742--1752.","author":"K\u00f6ster Urs","year":"2017","unstructured":"Urs K\u00f6ster , Tristan Webb , Xin Wang , Marcel Nassar , Arjun K Bansal , William Constable , Oguz Elibol , Scott Gray , Stewart Hall , Luke Hornof , 2017 . Flexpoint: An adaptive numerical format for efficient training of deep neural networks. In Advances in Neural Information Processing Systems (NIPS). 1742--1752. Urs K\u00f6ster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K Bansal, William Constable, Oguz Elibol, Scott Gray, Stewart Hall, Luke Hornof, et al. 2017. Flexpoint: An adaptive numerical format for efficient training of deep neural networks. In Advances in Neural Information Processing Systems (NIPS). 1742--1752."},{"key":"e_1_3_2_1_23_1","volume-title":"Imagenet Classification With Deep Convolutional Neural Networks. In 26th Annual Conference on Neural Information Processing Systems (NIPS). ACM, 1097--1105","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012 . Imagenet Classification With Deep Convolutional Neural Networks. In 26th Annual Conference on Neural Information Processing Systems (NIPS). ACM, 1097--1105 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet Classification With Deep Convolutional Neural Networks. In 26th Annual Conference on Neural Information Processing Systems (NIPS). ACM, 1097--1105."},{"key":"e_1_3_2_1_24_1","volume-title":"Deep learning. nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. nature 521, 7553 ( 2015 ), 436. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2015.03.008"},{"key":"e_1_3_2_1_26_1","first-page":"984","article-title":"Internet of things service architecture and method for realizing internet of things service","volume":"8","author":"Li Hui","year":"2015","unstructured":"Hui Li and Xiaojiang Xing . 2015 . Internet of things service architecture and method for realizing internet of things service . US Patent 8 , 984 ,113. Hui Li and Xiaojiang Xing. 2015. Internet of things service architecture and method for realizing internet of things service. US Patent 8,984,113.","journal-title":"US Patent"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-014-9492-7"},{"key":"e_1_3_2_1_28_1","unstructured":"Ji Lin Yongming Rao Jiwen Lu and Jie Zhou. 2017. Run-time neural pruning. In Advances in Neural Information Processing Systems (NIPS). 2181--2191.   Ji Lin Yongming Rao Jiwen Lu and Jie Zhou. 2017. Run-time neural pruning. In Advances in Neural Information Processing Systems (NIPS). 2181--2191."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2737479"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski etal 2015. Human-level control through deep reinforcement learning. Nature 518 7540 (2015) 529.  Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. Nature 518 7540 (2015) 529.","DOI":"10.1038\/nature14236"},{"volume-title":"From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots. In 2017 ieee international conference on robotics and automation (icra)","author":"Pfeiffer Mark","key":"e_1_3_2_1_31_1","unstructured":"Mark Pfeiffer , Michael Schaeuble , Juan Nieto , Roland Siegwart , and Cesar Cadena . 2017. From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots. In 2017 ieee international conference on robotics and automation (icra) . IEEE , 1527--1533. Mark Pfeiffer, Michael Schaeuble, Juan Nieto, Roland Siegwart, and Cesar Cadena. 2017. From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots. In 2017 ieee international conference on robotics and automation (icra). IEEE, 1527--1533."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2773600"},{"key":"e_1_3_2_1_34_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al.","author":"Silver David","year":"2016","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. 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."},{"key":"e_1_3_2_1_35_1","volume-title":"Two-Stream Convolutional Networks for Action Recognition in Videos. In NIPS'14","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014 . Two-Stream Convolutional Networks for Action Recognition in Videos. In NIPS'14 . ACM, 568--576. Karen Simonyan and Andrew Zisserman. 2014. Two-Stream Convolutional Networks for Action Recognition in Videos. In NIPS'14. ACM, 568--576."},{"key":"e_1_3_2_1_36_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations. ACM.","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015 . Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations. ACM. Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations. ACM."},{"key":"e_1_3_2_1_37_1","volume-title":"Asheesh Kumar Singh, and Soumik Sarkar","author":"Singh Arti","year":"2016","unstructured":"Arti Singh , Baskar Ganapathysubramanian , Asheesh Kumar Singh, and Soumik Sarkar . 2016 . Machine learning for high-throughput stress phenotyping in plants. Trends in plant science 21, 2 (2016), 110--124. Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, and Soumik Sarkar. 2016. Machine learning for high-throughput stress phenotyping in plants. Trends in plant science 21, 2 (2016), 110--124."},{"key":"e_1_3_2_1_38_1","volume-title":"Proceeding Deep Learning and Unsupervised Feature Learning NIPS Workshop","volume":"1","author":"Vanhoucke Vincent","year":"2011","unstructured":"Vincent Vanhoucke , Andrew Senior , and Mark Z Mao . 2011 . Improving the Speed of Neural Networks on CPUs . In Proceeding Deep Learning and Unsupervised Feature Learning NIPS Workshop , Vol. 1 . ACM, 4. Vincent Vanhoucke, Andrew Senior, and Mark Z Mao. 2011. Improving the Speed of Neural Networks on CPUs. In Proceeding Deep Learning and Unsupervised Feature Learning NIPS Workshop, Vol. 1. ACM, 4."},{"key":"e_1_3_2_1_39_1","unstructured":"Wei Wen Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2016. Learning structured sparsity in deep neural networks. In Advances in neural information processing systems. 2074--2082.   Wei Wen Chunpeng Wu Yandan Wang Yiran Chen and Hai Li. 2016. Learning structured sparsity in deep neural networks. In Advances in neural information processing systems. 2074--2082."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080215"}],"event":{"name":"PEARC '19: Practice and Experience in Advanced Research Computing","acronym":"PEARC '19","location":"Chicago IL USA"},"container-title":["Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3332186.3333049","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3332186.3333049","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3332186.3333049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:38Z","timestamp":1750206338000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3332186.3333049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,28]]},"references-count":40,"alternative-id":["10.1145\/3332186.3333049","10.1145\/3332186"],"URL":"https:\/\/doi.org\/10.1145\/3332186.3333049","relation":{},"subject":[],"published":{"date-parts":[[2019,7,28]]},"assertion":[{"value":"2019-07-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}