{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:33:46Z","timestamp":1770842026341,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031236174","type":"print"},{"value":"9783031236181","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-23618-1_36","type":"book-chapter","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:05:49Z","timestamp":1675062349000},"page":"545-556","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hierarchical Design Space Exploration for\u00a0Distributed CNN Inference at\u00a0the\u00a0Edge"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4540-9013","authenticated-orcid":false,"given":"Xiaotian","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2043-4469","authenticated-orcid":false,"given":"Andy D.","family":"Pimentel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6006-9366","authenticated-orcid":false,"given":"Todor","family":"Stefanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"36_CR1","unstructured":"AutoDiCE: https:\/\/github.com\/parrotsky\/autodice"},{"key":"36_CR2","unstructured":"Bai, J., et al.: ONNX: open neural network exchange (2019). https:\/\/github.com\/onnx\/onnx"},{"issue":"2","key":"36_CR3","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002). https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"36_CR4","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1007\/978-3-662-43505-2_49","volume-title":"Springer Handbook of Computational Intelligence","author":"K Deb","year":"2015","unstructured":"Deb, K.: Multi-objective evolutionary algorithms. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 995\u20131015. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-43505-2_49"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Dillon, T., Wu, C., Chang, E.: Cloud computing: Issues and challenges. In: 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 27\u201333 (2010)","DOI":"10.1109\/AINA.2010.187"},{"key":"36_CR6","unstructured":"Guo, Y.: A survey on methods and theories of quantized neural networks. arXiv preprint arXiv:1808.04752 (2018)"},{"issue":"6","key":"36_CR7","doi-asserted-by":"publisher","first-page":"4950","DOI":"10.1109\/JIOT.2020.2972000","volume":"7","author":"R Hadidi","year":"2020","unstructured":"Hadidi, R., Cao, J., Ryoo, M.S., Kim, H.: Toward collaborative inferencing of deep neural networks on Internet-of-Things devices. IEEE Internet Things J. 7(6), 4950\u20134960 (2020)","journal-title":"IEEE Internet Things J."},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Hou, X., Guan, Y., Han, T., Zhang, N.: Distredge: speeding up convolutional neural network inference on distributed edge devices. ArXiv abs\/2202.01699 (2022)","DOI":"10.1109\/IPDPS53621.2022.00110"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Kang, et al.: Neurosurgeon: collaborative intelligence between the cloud and mobile edge. ACM SIGARCH Comput. Archit. News 45(1), 615\u2013629 (2017)","DOI":"10.1145\/3093337.3037698"},{"key":"36_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.102989","volume":"73","author":"M Loni","year":"2020","unstructured":"Loni, M., Sinaei, S., Zoljodi, A., Daneshtalab, M., Sj\u00f6din, M.: Deepmaker: a multi-objective optimization framework for deep neural networks in embedded systems. Microprocess. Microsyst. 73, 102989 (2020)","journal-title":"Microprocess. Microsyst."},{"issue":"2","key":"36_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3488718","volume":"21","author":"S Minakova","year":"2022","unstructured":"Minakova, S., Sapra, D., Stefanov, T., Pimentel, A.D.: Scenario based run-time switching for adaptive CNN-based applications at the edge. ACM Trans. Embed. Comput. Syst. 21(2), 1\u201333 (2022)","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"36_CR13","unstructured":"NVIDIA: Jetson Xavier NX (2020). https:\/\/developer.nvidia.com\/embedded\/jetson-xavier-nx"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Pang, L.M., Ishibuchi, H., Shang, K.: NSGA-II with simple modification works well on a wide variety of many-objective problems. IEEE Access 8 (2020)","DOI":"10.1109\/ACCESS.2020.3032240"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Pimentel, A.: Exploring exploration: a tutorial introduction to embedded systems design space exploration. IEEE Design Test 34(1), 77\u201390 (2 2017)","DOI":"10.1109\/MDAT.2016.2626445"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Stahl, R., Zhao, Z., Mueller-Gritschneder, D., Gerstlauer, A., Schlichtmann, U.: Fully distributed deep learning inference on resource-constrained edge devices. In: International Conference on Embedded Computer Systems, pp. 77\u201390 (2019)","DOI":"10.1007\/978-3-030-27562-4_6"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Teerapittayanon, S., McDanel, B., Kung, H.T.: Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 328\u2013339. IEEE (2017)","DOI":"10.1109\/ICDCS.2017.226"},{"issue":"2","key":"36_CR18","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/TNET.2020.3042320","volume":"29","author":"L Zeng","year":"2020","unstructured":"Zeng, L., et al.: CoEdge: cooperative DNN inference with adaptive workload partitioning over heterogeneous edge devices. IEEE\/ACM Trans. Netw. 29(2), 595\u2013608 (2020)","journal-title":"IEEE\/ACM Trans. Netw."},{"issue":"11","key":"36_CR19","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TCAD.2018.2858384","volume":"37","author":"Z Zhao","year":"2018","unstructured":"Zhao, Z., Barijough, K.M., Gerstlauer, A.: DeepThings: distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Trans. Comput. Aided Design Integr. Circ. Syst. 37(11), 2348\u20132359 (2018)","journal-title":"IEEE Trans. Comput. Aided Design Integr. Circ. Syst."},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Zhou, L., et al.: Adaptive parallel execution of deep neural networks on heterogeneous edge devices. In: 4th ACM\/IEEE Symposium on Edge Computing, pp. 195\u2013208 (2019)","DOI":"10.1145\/3318216.3363312"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23618-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:16:35Z","timestamp":1675062995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23618-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031236174","9783031236181"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23618-1_36","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"236","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}