[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126002-en":3,"doc-seo-126002-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},126002,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Software Design Decisions for Greener Machine Learning-based Systems - Research thesis abstract","Widespread adoption of Machine Learning in software systems advances capabilities while increasing energy use and ecological impact. This thesis investigates how design decisions in ML-based systems influence energy consumption, with emphasis on deployment architecture and the training environment. Multiple case studies will validate and demonstrate how these choices affect training and operation energy demand. Results are expected to provide actionable insights, rigorous evaluations, and an energy prediction tool to support greener decision-making across the ML lifecycle.","Software Design Decisions for Greener Machine Learning-based  \nSystems  \nSantiago del Rey  \n[santiago.del.rey@upc.edu](santiago.del.rey@upc.edu)  \nUniversitat Politècnica de Catalunya  \nBarcelona, Spain  \nABSTRACT  \nThe widespread integration of Machine Learning (ML) in software systems has brought forth unprecedented advancements, yet the surge in energy consumption raises ecological concerns. This research addresses the environmental impact of ML development, focusing on the energy implications of design decisions in ML-based systems. This thesis aims to offer insights into the energy consumption patterns influenced by deployment architecture and training environment. Different case studies on ML-based systems will be conducted to validate and demonstrate the implications of these design choices. The expected outcomes encompass actionable insights, validated through rigorous evaluations, and the development of an energy prediction tool for ML-based system development, to help in the decision-making process. This work contributes to the broader field of Green AI by addressing a critical gap and guiding the transition towards a more sustainable AI landscape.  \nCCS CONCEPTS  \n• Software and its engineering; • Hardware → Impact on the environment; • Computing methodologies → Machine learning;  \nKEYWORDS  \nGreen AI, energy efficiency, software engineering, sustainable computing  \nACM Reference Format:  \nSantiago del Rey. 2024. Software Design Decisions for Greener Machine Learning-based Systems. In Conference on AI Engineering Software Engineering for AI (CAIN 2024), April 14–15, 2024, Lisbon, Portugal. ACM, New York, NY, USA, 3 pages. [https://doi.org/10.1145/3644815.3644972](https://doi.org/10.1145/3644815.3644972)  \n1 INTRODUCTION  \nThere is a growing concern about the environmental impact of machine learning (ML) models concerning climate change. The electricity consumed by Meta, Amazon, Microsoft, and Google, the four major suppliers of commercially available cloud computation, more than doubled between 2017 and 2021. World data center power use has increased by 20-40% per year in recent years, reaching 1-1.3% of world electricity demand and producing 1% of energy-related greenhouse gas (GHG) emissions in 2022 [14] .  \nCAIN 2024, April 14–15, 2024, Lisbon, Portugal © 2024 Copyright held by the owner/author(s) .  \nThis is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Conference on AI Engineering Software Engineering for AI (CAIN 2024), April 14–15, 2024, Lisbon, Portugal, [https://doi.org/10.1145/3644815.3644972](https://doi.org/10.1145/3644815.3644972) .  \nIt is unclear the total contribution of ML to the data center electricity consumption. Regardless, the high resource demands of ML systems are sure to contribute to the increasing electricity consumption and GHG emissions of data centers. To contextualize ML model energy consumption, a human’s yearly energy usage equates to about 11,023 lbs of CO2 e. In contrast, training a large transformer model emits 626,155 lbs of CO2 e [16], emphasizing the substantial environmental impact of ML.  \nConsidering that each ML model being developed today could be used for years in billions of machines, consuming energy and contributing to climate change, highlights the need for a transition to amore sustainable ML. For this transition to happen, we need a better understanding of the elements impacting energy consumption in ML-based systems.  \n2 PROBLEM STATEMENT  \nThe current research landscape in ML systems lacks comprehensive investigations into the intricate interplay between energy consumption and various design decisions. While the field has made notable strides [17], there remains a lack of studies delving deeper to unravel the nuanced ways in which energy usage is influenced by distinct design choices.  \nOne significant gap lies in the vague understanding of the repercussions asso","cbCairNaGO7QGc9q","https://ap.wps.com/l/cbCairNaGO7QGc9q","pdf",392945,1,3,"English","en",105,"# Abstract\n## Introduction\n## Problem statement\n## Research questions\n## Expected outcomes\n## Planned evaluation","[{\"question\":\"What problem does the thesis target regarding greener machine learning?\",\"answer\":\"The thesis targets the environmental impact of ML development by examining how energy consumption relates to design decisions and contributes to data center emissions.\"},{\"question\":\"Which design factors are examined in the research?\",\"answer\":\"The research focuses on deployment architecture and the training environment as key factors influencing training and operation energy consumption.\"},{\"question\":\"How will the thesis validate its research questions?\",\"answer\":\"The thesis will conduct different case studies on ML-based systems and use rigorous evaluations to demonstrate the implications of the examined design choices.\"}]","Software Design Decisions for Greener Machine Learning-based Systems - 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