[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291168-105":59,"doc-detail-291168-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","is-tinyml-sustainable-assessing-the-environmental-impacts-of-machine-learning-on-microcontrollers","Is TinyML Sustainable? - Assessing the Environmental Impacts of Machine Learning on Microcontrollers","","Sustained growth in carbon emissions and global waste raises major sustainability concerns, amplified by the expanding Internet of Things (IoT). Tiny Machine Learning (TinyML) enables machine learning on low-cost, low-power microcontroller systems, supporting always-on, on-device sensor analytics. Using a complete life cycle analysis (LCA), the work evaluates how TinyML applications may offset carbon emissions by reducing emissions in other sectors, while also showing that large-scale deployment still carries a non-negligible footprint. Designers must incorporate lifecycle environmental impacts when creating new devices, and the study outlines future research directions for greener TinyML.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/is-tinyml-sustainable-assessing-the-environmental-impacts-of-machine-learning-on-microcontrollers/291168/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/is-tinyml-sustainable-assessing-the-environmental-impacts-of-machine-learning-on-microcontrollers/291168.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is TinyML and how does it work on microcontrollers?","Question",{"text":113,"@type":114},"TinyML deploys machine learning algorithms on low-cost, low-power, resource-constrained microcontroller units. Models are stored in memory (e.g., flash) and inference runs directly on sensor outputs, reducing reliance on cloud communication for data processing.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the paper evaluate TinyML’s sustainability impact?",{"text":118,"@type":114},"The paper uses a complete life cycle analysis (LCA) to assess environmental impacts across the full system and application lifecycle, including both operational benefits and broader footprint effects.",{"name":120,"@type":111,"acceptedAnswer":121},"What do the results suggest about carbon reduction when TinyML is scaled?",{"text":122,"@type":114},"TinyML can enable applications that reduce emissions from other sectors, offering opportunities to offset its own carbon emissions. However, when globally scaled, TinyML’s carbon footprint remains non-negligible, so environmental impact must be considered during device design.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291168,1789643585,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","Is TinyML Sustainable?  \nAssessing the Environmental Impacts of Machine Learning on Microcontrollers  \nShvetank Prakash  \nHarvard University[sprakash@g.harvard.edu](sprakash@g.harvard.edu), USA  \nMatthew Stewart  \nHarvard University [matthew_stewart@g.harvard.edu](matthew_stewart@g.harvard.edu)[ ](matthew_stewart@g.harvard.edu)USA  \nColby Banbury  \nHarvard University [cbanbury@g.harvard.edu](cbanbury@g.harvard.edu), USA  \narXiv :2301 . 11899v2 [ cs .LG] 19 May 2023  \nMark Mazumder  \nHarvard University [markmazumder@g.harvard.edu](markmazumder@g.harvard.edu), USA  \nPete Warden  \nStanford University [petewarden@stanford.edu](petewarden@stanford.edu), USA  \nBrian Plancher  \nBarnard College, Columbia University [bplancher@barnard.edu](bplancher@barnard.edu), USA  \nVijay Janapa Reddi  \nHarvard University [vj@eecs.harvard.edu](vj@eecs.harvard.edu), USA  \nAbstract  \nThe sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment’s future. The growing Internet of Things (IoT) has the potential to exacerbate this issue. However, an emerging area known as Tiny Machine Learning (TinyML) has the opportunity to help address these environmental challenges through sustainable computing practices. TinyML, the deployment of machine learning (ML) algorithms onto low-cost, low-power microcontroller systems, enables on-device sensor analytics that unlocks numerous always-on ML applications. This article discusses both the potential of these TinyML applications to address critical sustainability challenges, as well as the environmental footprint of this emerging technology. Through a complete life cycle analysis (LCA), we find that TinyML systems present opportunities to offset their carbon emissions by enabling applications that reduce the emissions of other sectors. Nevertheless, when globally scaled, the carbon footprint of TinyML systems is not negligible, necessitating that designers factor in environmental impact when formulating new devices. Finally, we outline research directions to enable further sustainable contributions of TinyML.  \n1 Introduction  \nThe continued growth of carbon emissions and global waste presents a great concern for our environment, increasing calls for a more sustainable future. In response, the United Nations’(UN) 2030 Agenda for Sustainable Development established a shared framework aiming toward peace and prosperity for people and the planet. At its core are 17 Sustainable Development Goals (SDGs) [48], a call to action for all countries to work towards a more environmentally, economically, and socially sustainable future.  \nTiny machine learning (TinyML), which enables ML on microcontroller devices, holds potential for addressing numerous UN Sustainable Development Goals, particularly those related to environmental sustainability (see Figure 1) . While  \nLogistics Costs  \nMicrocontrollers  \nRaw  \nEnergy Materials  \nManufacturing  \nEnd-of-life  \nE-waste  \nMaintain  \nRecycling  \nUpcycling  \nDeploy  \nFigure 1. We show the positive (green arrows) and negative (red arrows) environmental footprint of the complete life cycle of TinyML systems as well as how TinyML can contribute to the UN’s environmental sustainability goals. TinyML’s operational benefits for sustainability are often highlighted, it is crucial to consider the entire life cycle of both applications and hardware to ensure a net carbon reduction. This paper contributes by (1) presenting case studies illustrating TinyML’s sustainability benefits,(2) examining the environmental impacts of TinyML at both MCU and system levels through a life cycle analysis (LCA), and (3) identifying future research directions for sustainable TinyML.  \n2 Tiny Machine Learning (TinyML)  \nTinyML is the deployment of machine learning (ML) algorithms onto low-cost, low-power, and resource-constrained MCU systems. TinyML stores neural network models directly within memory (e.g., flash) and runs inference directly on the outpu","cbCaiaKqgYmN0nCH","https://ap.wps.com/l/cbCaiaKqgYmN0nCH","pdf",3691217,"English","# Introduction\n## Tiny Machine Learning (TinyML)","[{\"question\":\"What is TinyML and how does it work on microcontrollers?\",\"answer\":\"TinyML deploys machine learning algorithms on low-cost, low-power, resource-constrained microcontroller units. Models are stored in memory (e.g., flash) and inference runs directly on sensor outputs, reducing reliance on cloud communication for data processing.\"},{\"question\":\"How does the paper evaluate TinyML’s sustainability impact?\",\"answer\":\"The paper uses a complete life cycle analysis (LCA) to assess environmental impacts across the full system and application lifecycle, including both operational benefits and broader footprint effects.\"},{\"question\":\"What do the results suggest about carbon reduction when TinyML is scaled?\",\"answer\":\"TinyML can enable applications that reduce emissions from other sectors, offering opportunities to offset its own carbon emissions. However, when globally scaled, TinyML’s carbon footprint remains non-negligible, so environmental impact must be considered during device design.\"}]","Is TinyML Sustainable? - Assessing the Environmental Impacts of Machine Learning on Microcontrollers | PDF",25]