{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:34:01Z","timestamp":1742949241894,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031490071"},{"type":"electronic","value":"9783031490088"}],"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\/978-3-031-49008-8_26","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:04:15Z","timestamp":1702559055000},"page":"323-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Measuring Latency-Accuracy Trade-Offs in\u00a0Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0050-3199","authenticated-orcid":false,"given":"Andr\u00e9","family":"Tse","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1036-1072","authenticated-orcid":false,"given":"Lino","family":"Oliveira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6219-3977","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Vinagre","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"26_CR1","unstructured":"Bolukbasi, T., Wang, J., Dekel, O., Saligrama, V.: Adaptive neural networks for efficient inference. In: International Conference on Machine Learning, pp. 527\u2013536. PMLR (2017)"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Chang, S.E., Li, Y., Sun, M., Shi, R., So, H.K.H., Qian, X., Wang, Y., Lin, X.: Mix and match: a novel FPGA-centric deep neural network quantization framework. In: 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pp. 208\u2013220. IEEE (2021)","DOI":"10.1109\/HPCA51647.2021.00027"},{"key":"26_CR3","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.future.2022.04.031","volume":"135","author":"T Choudhary","year":"2022","unstructured":"Choudhary, T., Mishra, V., Goswami, A., Sarangapani, J.: Inference-aware convolutional neural network pruning. Futur. Gener. Comput. Syst. 135, 44\u201356 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Dong, Z., Yao, Z., Gholami, A., Mahoney, M.W., Keutzer, K.: Hawq: Hessian aware quantization of neural networks with mixed-precision. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 293\u2013302 (2019)","DOI":"10.1109\/ICCV.2019.00038"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Fu, C., Zhu, S., Su, H., Lee, C.E., Zhao, J.: Towards fast and energy-efficient binarized neural network inference on FPGA (2018). arXiv:1810.02068","DOI":"10.1145\/3289602.3293990"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Hacene, G.B., Gripon, V., Arzel, M., Farrugia, N., Bengio, Y.: Quantized guided pruning for efficient hardware implementations of deep neural networks. In: 2020 18th IEEE International New Circuits and Systems Conference (NEWCAS), pp. 206\u2013209. IEEE (2020)","DOI":"10.1109\/NEWCAS49341.2020.9159769"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Lebedev, V., Lempitsky, V.: Speeding-up convolutional neural networks: a survey. Bull. Pol. Acad. Sci.: Tech. Sci. 66(6) (2018)","DOI":"10.24425\/bpas.2018.125927"},{"key":"26_CR8","unstructured":"LeCun, Y., Denker, J., Solla, S.: Optimal brain damage. In: Advances in Neural Information Processing Systems, vol. 2 (1989)"},{"key":"26_CR9","unstructured":"Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient inference (2016). arXiv:1611.06440"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Nasr, M., Bahramali, A., Houmansadr, A.: Deepcorr: strong flow correlation attacks on tor using deep learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1962\u20131976 (2018)","DOI":"10.1145\/3243734.3243824"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Panchenko, A., Lanze, F., Engel, T.: Improving performance and anonymity in the tor network. In: 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC), pp. 1\u201310. IEEE (2012)","DOI":"10.1109\/PCCC.2012.6407715"},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"174129","DOI":"10.1109\/ACCESS.2019.2952577","volume":"7","author":"TA Putra","year":"2019","unstructured":"Putra, T.A., Leu, J.S.: Multilevel neural network for reducing expected inference time. IEEE Access 7, 174129\u2013174138 (2019)","journal-title":"IEEE Access"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Teerapittayanon, S., McDanel, B., Kung, H.T.: Branchynet: Fast inference via early exiting from deep neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2464\u20132469. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900006"},{"issue":"4","key":"26_CR14","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611\u2013629 (2018)","journal-title":"Insights Imaging"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49008-8_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T23:51:42Z","timestamp":1730850702000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49008-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031490071","9783031490088"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49008-8_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faial Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","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":"epia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2023.inesctec.pt\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"163","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":"85","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":"52% - 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":"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":"2","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}