[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119163-en":3,"doc-seo-119163-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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":29},119163,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Tiny Machine Learning - Progress and Futures","Tiny Machine Learning (TinyML) focuses on running deep learning models on billions of IoT devices and microcontrollers, enabling ubiquitous, always-on intelligence at the edge. The field is constrained by tiny memory footprints and limited compiler and inference support for bare-metal hardware, which prevents straightforward scaling from cloud or mobile platforms. A system-algorithm co-design approach is required. The review covers core definitions, key challenges and applications, recent progress, MCUNet for ImageNet-scale inference, advances toward on-device training, and future research directions.","Tiny Machine Learning: Progress and Futures  \nJi Lin Ligeng Zhu Wei-Ming Chen Wei-Chen Wang Song Han Massachusetts Institute of Technology  \n[https://tinyml.mit.edu](https://tinyml.mit.edu)  \narXiv :2403 . 19076v2 [ cs .LG] 29 Mar 2024  \nAbstract—Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence. However, TinyML is challenging due to hardware constraints: the tiny memory resource makes it difficult to hold deep learning models designed for cloud and mobile platforms. There is also limited compiler and inference engine support for bare-metal devices. Therefore, we need to co-design the algorithm and system stack to enable TinyML. In this review, we will first discuss the definition, challenges, and applications of TinyML. We then survey the recent progress in TinyML and deep learning on MCUs. Next, we will introduce MCUNet, showing how we can achieve ImageNet-scale AI applications on IoT devices with system-algorithm co-design. We will further extend the solution from inference to training and introduce tiny on-device training techniques. Finally, we present future directions in this area. Today’s “large” model might be tomorrow’s “tiny” model. The scope of TinyML should evolve and adapt over time.  \nIndex Terms—TinyML, Efficient Deep Learning, On-Device Training, Learning on the Edge  \nI. OVERVIEW OF TINY MACHINE LEARNING  \nMachine learning (ML) has made significant impacts on various fields, including vision, language, and audio. However, state-of-the-art models often come at the cost of high computation and memory, making them expensive to deploy. To address this, researchers have been working on efficient algorithms, systems, and hardware to reduce the cost of machine learning models in various deployment scenarios. There are two main subdomains of efficient ML: EdgeML and CloudML (Figure 1) . While CloudML focuses on improving latency and throughput on cloud servers, EdgeML focuses on improving energy efficiency, latency, and privacy on edge devices. These two domains also intersect in areas such as hybrid inference [1, 2], over-the-air (OTA) updates, and federated learning between the edge and cloud [3] . In recent years, there has been significant progress in extending the scope of EdgeML to ultra-low-power devices such as IoT devices and microcontrollers, known as TinyML.  \nTinyML has several key advantages. It enables machine learning using only a few hundred kilobytes of memory which greatly reduces the cost. With billions of IoT devices producing more and more data in our daily lives, there is a growing need for low-power, always-on, on-device AI. By performing on-device inference near the sensor, TinyML enables better  \nThis paper is published by IEEE Circuits and Systems Magazine. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nFig. 1. Efficiency is critical for CloudML, EdgeML, and TinyML. CloudML targets high-throughput accelerators like GPUs, while EdgeML focuses on portable devices like mobile phones. TinyML further pushes the efficiency boundary, enabling powerful ML models to run on ultra-low-power devices such as microcontrollers.  \nresponsiveness and privacy while reducing the energy cost associated with wireless communication. On-device processing of data can be beneficial for applications where real-time decision-making is crucial, such as autonomous vehicles.  \nIn addition to inference, we push the frontier of TinyML to enable on-device training on IoT devices. Itrevolutionizes EdgeAI throu","cbCaiclEbzyzf3xp","https://ap.wps.com/l/cbCaiclEbzyzf3xp","pdf",19084284,1,24,"English","en",105,"# Overview of Tiny Machine Learning\n## Efficient ML subdomains: EdgeML and CloudML\n## TinyML advantages and use cases\n# Challenges of TinyML\n## Why cloud/mobile models can’t scale to MCUs","[{\"question\":\"What is Tiny Machine Learning (TinyML) and why is it important?\",\"answer\":\"TinyML enables machine learning to run on ultra-low-power microcontrollers and IoT devices with very limited memory. It supports always-on, on-device intelligence while improving responsiveness and helping protect privacy.\"},{\"question\":\"What major challenges prevent direct deployment of cloud/mobile deep learning models on tiny devices?\",\"answer\":\"TinyML faces hardware constraints such as extremely small SRAM/FLASH, lack of DRAM, and minimal operating system support. Limited compiler and inference engine support also makes bare-metal deployment difficult.\"},{\"question\":\"How does MCUNet contribute to TinyML progress?\",\"answer\":\"MCUNet demonstrates system-algorithm co-design to achieve ImageNet-scale AI applications on IoT devices, extending solutions from efficient inference toward training as well.\"}]","Tiny Machine Learning - Progress and Futures | PDF",1785722856,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"tiny-machine-learning-progress-and-futures","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/tiny-machine-learning-progress-and-futures/119163/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is Tiny Machine Learning (TinyML) and why is it important?","Question",{"text":75,"@type":76},"TinyML enables machine learning to run on ultra-low-power microcontrollers and IoT devices with very limited memory. It supports always-on, on-device intelligence while improving responsiveness and helping protect privacy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What major challenges prevent direct deployment of cloud/mobile deep learning models on tiny devices?",{"text":80,"@type":76},"TinyML faces hardware constraints such as extremely small SRAM/FLASH, lack of DRAM, and minimal operating system support. Limited compiler and inference engine support also makes bare-metal deployment difficult.",{"name":82,"@type":73,"acceptedAnswer":83},"How does MCUNet contribute to TinyML progress?",{"text":84,"@type":76},"MCUNet demonstrates system-algorithm co-design to achieve ImageNet-scale AI applications on IoT devices, extending solutions from efficient inference toward training as well.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]