{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:01:31Z","timestamp":1750309291516,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"publisher","award":["BDNS 657443"],"award-info":[{"award-number":["BDNS 657443"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci\u00f3n","doi-asserted-by":"publisher","award":["TED2021-130923B-I00"],"award-info":[{"award-number":["TED2021-130923B-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,14]]},"DOI":"10.1145\/3644815.3644972","type":"proceedings-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T17:28:38Z","timestamp":1718126918000},"page":"256-258","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Software Design Decisions for Greener Machine Learning-based Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4979-414X","authenticated-orcid":false,"given":"Santiago","family":"del Rey","sequence":"first","affiliation":[{"name":"Universitat Polit\u00e8cnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.sustainlp-1.2"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/2515-7620\/acf81b"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1134\/S1064562422060230"},{"key":"e_1_3_2_1_4_1","volume-title":"Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads. In High Performance Computing. ISC High Performance 2022 International Workshops. 108--121","author":"Caspart Ren\u00e9","year":"2022","unstructured":"Ren\u00e9 Caspart, Sebastian Ziegler, Arvid Weyrauch, Holger Obermaier, Simon Raffeiner, Leon Pascal Schuhmacher, Jan Scholtyssek, Darya Trofimova, Marco Nolden, Ines Reinartz, Fabian Isensee, Markus G\u00f6tz, and Charlotte Debus. 2022. Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads. In High Performance Computing. ISC High Performance 2022 International Workshops. 108--121."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/SEAA60479.2023.00031"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/su132212392"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","unstructured":"Alexandre Lacoste Alexandra Luccioni Victor Schmidt and Thomas Dandres. 2019. Quantifying the Carbon Emissions of Machine Learning. (2019). arXiv:1910.09700 [cs] 10.48550\/arXiv.1910.09700","DOI":"10.48550\/arXiv.1910.09700"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202100707"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","unstructured":"Da Li Xinbo Chen Michela Becchi and Ziliang Zong. 2016. Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs. In BDCloud-SocialCom-SustainCom. 477--484. 10.1109\/BDCloud-SocialCom-SustainCom.2016.76","DOI":"10.1109\/BDCloud-SocialCom-SustainCom.2016.76"},{"key":"e_1_3_2_1_10_1","volume-title":"Power Hungry Processing: Watts Driving the Cost of AI Deployment?","author":"Luccioni Alexandra Sasha","year":"2023","unstructured":"Alexandra Sasha Luccioni, Yacine Jernite, and Emma Strubell. 2023. Power Hungry Processing: Watts Driving the Cost of AI Deployment? (2023). arXiv:2311.16863 [cs] Retrieved 2023-12-04 from http:\/\/arxiv.org\/abs\/2311.16863"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7841045"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2020.2995135"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2022.3148714"},{"key":"e_1_3_2_1_14_1","volume-title":"Data Centres & Networks","author":"Rozite Vida","year":"2023","unstructured":"Vida Rozite, EmiBertoli, and Brendan Reidenbach. 2023. Data Centres & Networks. IEA. Retrieved 2023-12-15 from https:\/\/www.iea.org\/energy-system\/buildings\/data-centres-and-data-transmission-networks"},{"key":"e_1_3_2_1_15_1","unstructured":"Victor Schmidt Kamal Goyal Aditya Joshi Boris Feld Liam Conell Nikolas Laskaris Doug Blank Jonathan Wilson Sorelle Friedler and Sasha Luccioni. 2021. CodeCarbon: Estimate and Track Carbon Emissions from Machine Learning Computing. Retrieved 2023-12-12 from https:\/\/mlco2.github.io\/codecarbon\/index.html"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Emma Strubell Ananya Ganesh and Andrew McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. (2019). arXiv:1906.02243","DOI":"10.18653\/v1\/P19-1355"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1507"},{"key":"e_1_3_2_1_18_1","first-page":"795","article-title":"Sustainable AI: Environmental Implications, Challenges and Opportunities","volume":"4","author":"Wu Carole-Jean","year":"2022","unstructured":"Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, and Kim Hazelwood. 2022. Sustainable AI: Environmental Implications, Challenges and Opportunities. In Proceedings of Machine Learning and Systems, Vol. 4. 795--813.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.14604"}],"event":{"name":"CAIN 2024: IEEE\/ACM 3rd International Conference on AI Engineering - Software Engineering for AI","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering"],"location":"Lisbon Portugal","acronym":"CAIN 2024"},"container-title":["Proceedings of the IEEE\/ACM 3rd International Conference on AI Engineering - Software Engineering for AI"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3644815.3644972","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3644815.3644972","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:27Z","timestamp":1750291407000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3644815.3644972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":19,"alternative-id":["10.1145\/3644815.3644972","10.1145\/3644815"],"URL":"https:\/\/doi.org\/10.1145\/3644815.3644972","relation":{},"subject":[],"published":{"date-parts":[[2024,4,14]]},"assertion":[{"value":"2024-06-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}