{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:14:50Z","timestamp":1775283290210,"version":"3.50.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030997359","type":"print"},{"value":"9783030997366","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-99736-6_18","type":"book-chapter","created":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T19:02:47Z","timestamp":1649098967000},"page":"260-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble Model Compression for\u00a0Fast and\u00a0Energy-Efficient Ranking on\u00a0FPGAs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4637-9725","authenticated-orcid":false,"given":"Veronica","family":"Gil-Costa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8552-1221","authenticated-orcid":false,"given":"Fernando","family":"Loor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7688-6248","authenticated-orcid":false,"given":"Romina","family":"Molina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3183-334X","authenticated-orcid":false,"given":"Franco\u00a0Maria","family":"Nardini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7189-4724","authenticated-orcid":false,"given":"Raffaele","family":"Perego","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6541-9409","authenticated-orcid":false,"given":"Salvatore","family":"Trani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"18_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-642-36973-5_13","volume-title":"Advances in Information Retrieval","author":"N Asadi","year":"2013","unstructured":"Asadi, N., Lin, J.: Training efficient tree-based models for document ranking. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 146\u2013157. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-36973-5_13"},{"key":"18_CR2","unstructured":"Bergstra, J., Yamins, D., Cox, D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: Proceedings of ICML, pp. 115\u2013123. PMLR (2013)"},{"issue":"23\u2013581","key":"18_CR3","first-page":"81","volume":"11","author":"CJ Burges","year":"2010","unstructured":"Burges, C.J.: From ranknet to lambdarank to lambdamart: an overview. Learning 11(23\u2013581), 81 (2010)","journal-title":"Learning"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Busolin, F., Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Trani, S.: Learning early exit strategies for additive ranking ensembles. In: Proceedings of SIGIR, pp. 2217\u20132221. ACM (2021)","DOI":"10.1145\/3404835.3463088"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Cambazoglu, B.B., et al.: Early exit optimizations for additive machine learned ranking systems. In: Proceedings of WSDM, pp. 411\u2013420. ACM (2010)","DOI":"10.1145\/1718487.1718538"},{"issue":"6","key":"18_CR6","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1016\/j.ipm.2016.05.004","volume":"52","author":"G Capannini","year":"2016","unstructured":"Capannini, G., Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Tonellotto, N.: Quality versus efficiency in document scoring with learning-to-rank models. Inf. Process. Manag. 52(6), 1161\u20131177 (2016)","journal-title":"Inf. Process. Manag."},{"key":"18_CR7","unstructured":"Chapelle, O., Chang, Y.: Yahoo! Learning to rank challenge overview. J. Mach. Learn. Res. 14, 1\u201324 (2011). Proceedings Track"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Chen, R.C., Gallagher, L., Blanco, R., Culpepper, J.S.: Efficient cost-aware cascade ranking in multi-stage retrieval. In: Proceedings of SIGIR, pp. 445\u2013454. ACM (2017)","DOI":"10.1145\/3077136.3080819"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of ACM SIGKDD, pp. 785\u2013794. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Dato, D., et al.: Fast ranking with additive ensembles of oblivious and non-oblivious regression trees. ACM Trans. Inf. Syst. 35(2), 15:1\u201315:31 (2016)","DOI":"10.1145\/2987380"},{"key":"18_CR11","first-page":"1189","volume":"29","author":"JH Friedman","year":"2000","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189\u20131232 (2000)","journal-title":"Ann. Stat."},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Gallagher, L., Chen, R.C., Blanco, R., Culpepper, J.S.: Joint optimization of cascade ranking models. In: Proceedings of WSDM, pp. 15\u201323. ACM (2019)","DOI":"10.1145\/3289600.3290986"},{"key":"18_CR13","first-page":"1","volume":"4","author":"R Gao","year":"2011","unstructured":"Gao, R., Hsu, F.H.: An FPGA-based accelerator for LambdaRank in web search engines. ACM TRETS 4, 1\u201319 (2011)","journal-title":"ACM TRETS"},{"key":"18_CR14","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural network with pruning, trained quantization and Huffman coding. In: Proceedings of ICLR (2016)"},{"issue":"4","key":"18_CR15","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422\u2013446 (2002)","journal-title":"ACM Trans. Inf. Syst."},{"key":"18_CR16","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of NIPS, pp. 3149\u20133157 (2017)"},{"issue":"9","key":"18_CR17","first-page":"2075","volume":"30","author":"F Lettich","year":"2019","unstructured":"Lettich, F., et al.: Parallel traversal of large ensembles of decision trees. IEEE TPDS 30(9), 2075\u20132089 (2019)","journal-title":"IEEE TPDS"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, E., Fleming, S.T., Thomas, D., Cheung, P.: Accelerating position-aware top-k ListNet for ranking under custom precision regimes. In: Proceedings of FPL, pp. 81\u201387 (2019)","DOI":"10.1109\/FPL.2019.00022"},{"issue":"3","key":"18_CR19","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1561\/1500000016","volume":"3","author":"TY Liu","year":"2009","unstructured":"Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225\u2013331 (2009)","journal-title":"Found. Trends Inf. Retr."},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Tonellotto, N., Venturini, R.: QuickScorer: a fast algorithm to rank documents with additive ensembles of regression trees. In: Proceedings of SIGIR, pp. 73\u201382. ACM (2015)","DOI":"10.1145\/2766462.2767733"},{"key":"18_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3205453","volume":"9","author":"C Lucchese","year":"2018","unstructured":"Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Silvestri, F., Salvatore, T.: X-CLEaVER: learning ranking ensembles by growing and pruning trees. ACM TIST 9, 1\u201326 (2018)","journal-title":"ACM TIST"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Silvestri, F., Trani, S.: Post-learning optimization of tree ensembles for efficient ranking. In: Proceedings of SIGIR, pp. 949\u2013952 (2016)","DOI":"10.1145\/2911451.2914763"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Tonellotto, N., Venturini, R.: Exploiting CPU SIMD extensions to speed-up document scoring with tree ensembles. In: Proceedings of ACM SIGIR, pp. 833\u2013836 (2016)","DOI":"10.1145\/2911451.2914758"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Trani, S.: X-DART: blending dropout and pruning for efficient learning to rank. In: Proceedings of SIGIR, pp. 1077\u20131080. ACM (2017)","DOI":"10.1145\/3077136.3080725"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Trani, S.: Query-level early exit for additive learning-to-rank ensembles. In: Proceedings of SIGIR, pp. 2033\u20132036. ACM (2020)","DOI":"10.1145\/3397271.3401256"},{"key":"18_CR26","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.jpdc.2021.04.008","volume":"155","author":"R Molina","year":"2021","unstructured":"Molina, R., Loor, F., Gil-Costa, V., Nardini, F.M., Perego, R., Trani, S.: Efficient traversal of decision tree ensembles with FPGAs. J. Parallel Distrib. Comput. 155, 38\u201349 (2021)","journal-title":"J. Parallel Distrib. Comput."},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Qasaimeh, M., Denolf, K., Lo, J., Vissers, K., Zambreno, J., Jones, P.H.: Comparing energy efficiency of CPU, GPU and FPGA implementations for vision kernels. In: Proceedings of IEEE ICESS, pp. 1\u20138 (2019)","DOI":"10.1109\/ICESS.2019.8782524"},{"key":"18_CR28","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.softx.2017.06.005","volume":"6","author":"DD Sensi","year":"2017","unstructured":"Sensi, D.D., Torquati, M., Danelutto, M.: Mammut: high-level management of system knobs and sensors. SoftwareX 6, 150\u2013154 (2017)","journal-title":"SoftwareX"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: Proceedings of CIKM. ACM (2007)","DOI":"10.1145\/1321440.1321528"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Wang, L., Lin, J., Metzler, D.: Learning to efficiently rank. In: Proceedings of SIGIR, pp. 138\u2013145. ACM, New York (2010)","DOI":"10.1145\/1835449.1835475"},{"key":"18_CR31","doi-asserted-by":"publisher","unstructured":"Wu, Q., Burges, C., Svore, K., Gao, J.: Adapting boosting for information retrieval measures. Inf. Retrieval 13, 254\u2013270 (2010). https:\/\/doi.org\/10.1007\/s10791-009-9112-1","DOI":"10.1007\/s10791-009-9112-1"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Xin, J., Tang, R., Yu, Y., Lin, J.: BERxiT: early exiting for BERT with better fine-tuning and extension to regression. In: Proceedings of ACL, pp. 91\u2013104. ACL, April 2021","DOI":"10.18653\/v1\/2021.eacl-main.8"},{"issue":"4","key":"18_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1462586.1462588","volume":"1","author":"NY Xu","year":"2009","unstructured":"Xu, N.Y., Cai, X.F., Gao, R., Zhang, L., Hsu, F.H.: FPGA acceleration of RankBoost in web search engines. ACM TRETS 1(4), 1\u201319 (2009)","journal-title":"ACM TRETS"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Ye, T., Zhou, H., Zou, W.Y., Gao, B., Zhang, R.: RapidScorer: fast tree ensemble evaluation by maximizing compactness in data level parallelization. In: Proceedings of SIGKDD, pp. 941\u2013950. ACM (2018)","DOI":"10.1145\/3219819.3219857"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-99736-6_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T08:56:38Z","timestamp":1710233798000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-99736-6_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030997359","9783030997366"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-99736-6_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"5 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stavanger","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","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":"10 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2022.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"395","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":"35","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":"29","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":"9% - 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","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":"4-6","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)"}},{"value":"Additionally, there are other papers: 11 reproducibility, 12 doctoral, 13 CLEF Labs, 5 workshops and 4 tutorials.","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)"}}]}}