{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:07Z","timestamp":1750309507730,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,23]]},"DOI":"10.1145\/3649329.3656225","type":"proceedings-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T19:27:22Z","timestamp":1731007642000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Duet: A Collaborative User Driven Recommendation System for Edge Devices"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5008-3049","authenticated-orcid":false,"given":"Vidushi","family":"Goyal","sequence":"first","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0319-3368","authenticated-orcid":false,"given":"Valeria","family":"Bertacco","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5894-8342","authenticated-orcid":false,"given":"Reetuparna","family":"Das","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"\"Deep dive into netflix's recommender system.\" https:\/\/towardsdatascience.com\/deep-dive-into-netflixs-recommender-system-341806ae3b48."},{"key":"e_1_3_2_1_2_1","unstructured":"\"How retailers can keep up with consumers.\" https:\/\/www.mckinsey.com\/industries\/retail\/our-insights\/how-retailers-can-keep-up-with-consumers."},{"key":"e_1_3_2_1_3_1","volume-title":"The architectural implications of facebook's dnn-based personalized recommendation,\" in HPCA","author":"Gupta U.","year":"2020","unstructured":"U. Gupta et al., \"The architectural implications of facebook's dnn-based personalized recommendation,\" in HPCA, 2020."},{"key":"e_1_3_2_1_4_1","volume-title":"A system for optimizing end-to-end at-scale neural recommendation inference,\" in ISCA","author":"Gupta U.","year":"2020","unstructured":"U. Gupta, S. Hsia, V. Saraph, X. Wang, B. Reagen, G.-Y. Wei, H.-H. S. Lee, D. Brooks, and C.-J. Wu, \"Deeprecsys: A system for optimizing end-to-end at-scale neural recommendation inference,\" in ISCA, 2020."},{"key":"e_1_3_2_1_5_1","volume-title":"Deep interest network for click-through rate prediction,\" in SIGKDD","author":"Zhou G.","year":"2018","unstructured":"G. Zhou et al., \"Deep interest network for click-through rate prediction,\" in SIGKDD, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"Understanding training efficiency of deep learning recommendation models at scale,\" in HPCA","author":"Acun B.","year":"2021","unstructured":"B. Acun et al., \"Understanding training efficiency of deep learning recommendation models at scale,\" in HPCA, 2021."},{"key":"e_1_3_2_1_7_1","volume-title":"Bandana: Using non-volatile memory for storing deep learning models,\" MLSys","author":"Eisenman A.","year":"2019","unstructured":"A. Eisenman et al., \"Bandana: Using non-volatile memory for storing deep learning models,\" MLSys, 2019."},{"key":"e_1_3_2_1_8_1","volume-title":"Deep learning recommendation model for personalization and recommendation systems,\" CoRR","author":"Naumov M.","year":"2019","unstructured":"M. Naumov et al., \"Deep learning recommendation model for personalization and recommendation systems,\" CoRR, vol. abs\/1906.00091, 2019."},{"key":"e_1_3_2_1_9_1","unstructured":"\"Criteo kaggele advertising dataset.\" https:\/\/ailab.criteo.com\/ressources\/."},{"key":"e_1_3_2_1_10_1","volume-title":"Van Den Hengel, \"Image-based recommendations on styles and substitutes,\" in SIGIR","author":"McAuley J.","year":"2015","unstructured":"J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel, \"Image-based recommendations on styles and substitutes,\" in SIGIR, 2015."},{"key":"e_1_3_2_1_11_1","unstructured":"\"Ad display\/click data on taobao.com.\" https:\/\/tianchi.aliyun.com\/dataset\/dataDetail?datald=56."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3452296.3472923"},{"key":"e_1_3_2_1_13_1","volume-title":"A 23.6-mb\/mm2 sram in 10-nm finfet technology with pulsed-pmos tvc and stepped-wl for low-voltage applications,\" JSSC","author":"Guo Z.","year":"2018","unstructured":"Z. Guo et al., \"A 23.6-mb\/mm2 sram in 10-nm finfet technology with pulsed-pmos tvc and stepped-wl for low-voltage applications,\" JSSC, 2018."},{"key":"e_1_3_2_1_14_1","first-page":"790","volume-title":"Recnmp: Accelerating personalized recommendation with near-memory processing,\" in ISCA","author":"Ke L.","year":"2020","unstructured":"L. Ke et al., \"Recnmp: Accelerating personalized recommendation with near-memory processing,\" in ISCA, pp. 790--803, 2020."},{"key":"e_1_3_2_1_15_1","volume-title":"Trim: Enhancing processor-memory interfaces with scalable tensor reduction in memory,\" in MICRO","author":"Park J.","year":"2021","unstructured":"J. Park et al., \"Trim: Enhancing processor-memory interfaces with scalable tensor reduction in memory,\" in MICRO, 2021."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412700"},{"key":"e_1_3_2_1_17_1","volume-title":"Myml: User-driven machine learning,\" in DAC","author":"Goyal V.","year":"2021","unstructured":"V. Goyal, V. Bertacco, and R. Das, \"Myml: User-driven machine learning,\" in DAC, 2021."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3037697.3037698"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358284"},{"key":"e_1_3_2_1_20_1","volume-title":"Centaur: A chiplet-based, hybrid sparse-dense accelerator for personalized recommendations,\" in ISCA","author":"Hwang R.","year":"2020","unstructured":"R. Hwang et al., \"Centaur: A chiplet-based, hybrid sparse-dense accelerator for personalized recommendations,\" in ISCA, 2020."},{"key":"e_1_3_2_1_21_1","volume-title":"Recpipe: Co-designing models and hardware to jointly optimize recommendation quality and performance,\" in MICRO","author":"Gupta U.","year":"2021","unstructured":"U. Gupta et al., \"Recpipe: Co-designing models and hardware to jointly optimize recommendation quality and performance,\" in MICRO, 2021."}],"event":{"name":"DAC '24: 61st ACM\/IEEE Design Automation Conference","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-CEDA","SIGBED ACM Special Interest Group on Embedded Systems"],"location":"San Francisco CA USA","acronym":"DAC '24"},"container-title":["Proceedings of the 61st ACM\/IEEE Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3656225","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649329.3656225","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649329.3656225","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:55Z","timestamp":1750295875000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3656225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":21,"alternative-id":["10.1145\/3649329.3656225","10.1145\/3649329"],"URL":"https:\/\/doi.org\/10.1145\/3649329.3656225","relation":{},"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"2024-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}