[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120682-en":3,"doc-seo-120682-105":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},120682,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",6,"Technology","EDGE IMPULSE - AN MLOPS PLATFORM FOR TINY MACHINE LEARNING","Edge Impulse is a cloud-based machine learning operations (MLOps) platform enabling development of embedded and edge ML (TinyML) systems for deployment across many hardware targets. TinyML workflows often suffer from fragmented software stacks and heterogeneous deployment hardware, which makes optimizations hard to reproduce and results in poor portability. The platform streamlines the TinyML design cycle with extensible tooling for software and hardware optimizations and reports large community adoption by Oct. 2022.","arXiv :2212 .03332v2 [ cs .DC] 21 Apr 2023  \nEDGE IMPULSE: AN MLOPS PLATFORM FOR TINY MACHINE LEARNING  \nShawn Hymel * Colby Banbury * Daniel Situnayake Alex Elium Carl Ward Mat Kelcey Mathijs Baaijens Mateusz Majchrzycki Jenny Plunkett David Tischler Alessandro Grande Louis Moreau Dmitry Maslov  \nArtie Beavis Jan Jongboom Vijay Janapa Reddi  \nABSTRACT  \nEdge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimizationsdifficult and unportable. We present Edge Impulse, a practical MLOps platform for developing TinyML systems at scale. Edge Impulse addresses these challenges and streamlines the TinyML design cycle by supporting various software and hardware optimizations to create an extensible and portable software stack for a multitude of embedded systems. As of Oct. 2022, Edge Impulse hosts 118,185 projects from 50,953 developers.  \n1 INTRODUCTION  \nMachine learning (ML) has become an increasingly important tool in embedded systems for solving difficult problems, enhancing existing Internet of Things (IoT) infrastructure, and offering unique ways to save power and bandwidth in sensor networks. ML inference on TinyML systems has facilitated the development of technologies in low-power devices such as wakeword detection (Gruenstein et al., 2017), predictive maintenance (Susto et al., 2015), anomaly detection (Koizumi et al., 2019), visual object detection (Chowdhery et al., 2019), and human activity recognition (Chavarriaga et al., 2013) . According to ABI Resarch, a global technology intelligence firm, the “installed base of devices with edge AI chipset will exceed 5 billion by 2025.” Additionally, the embedded ML market is expected to reach US$44.3 billion by 2027 (abi, 2021) .  \nDespite the promising advances, the embedded ML development ecosystem has lagged behind the demand for applications. The embedded ML development workflow often requires specific expertise. For instance, embedded ML developers often have to learn a new set of tools for training new models and porting them to an embedded framework written in C or C++, while managing conflicting library dependencies. Additionally, hardware vendor specific frameworks often lock a developer into a particular ecosystem,  \n*Equal Contribution  \nCorresponding author: \u003C[cbanbury@g.harvard.edu](cbanbury@g.harvard.edu) >.  \nColby Banbury and Vijay Janapa Reddi are with the John A. Paulson School of Engineering and Applied Sciences, Harvard University. All others are with Edge Impulse.  \nEdge Impulse website: [http://www](http://www:edgeimpulse:com/)[:](http://www:edgeimpulse:com/)[edgeimpulse](http://www:edgeimpulse:com/)[:](http://www:edgeimpulse:com/)[com/](http://www:edgeimpulse:com/)[ ](http://www:edgeimpulse:com/)Proceedings of the 5 th MLSys Conference, Santa Clara, CA, USA, 2022. Copyright 2022 by the author(s) .  \nwhich limits the flexibility and scalability of an application.  \nWhile popular frameworks such as TensorFlow Lite for Microcontrollers (TFLM) (David et al., 2021) help address optimization and compression of neural networks for embedded devices, adoption has been slow due to challenges that are unique to the embedded machine learning ecosystem. Broadly, these include the following:  \n1. Data collection challenge. There is no large-scale, curated, public sensor data set for the embedded ecosystem. Currently, it is difficult to efficiently collect, and analyze such datasets from a rich variety of sensors. Additionally, data cleaning and labeling are essential to ML development but are expensive, labor intensive processes without tooling or automation.  \n2. Data preprocessing challenge. Digital signal processing (DSP) is a critical stage of the ML stack and has strong interactions with the ML model, that are sometimes har","cbCait8yG924Lxpr","https://ap.wps.com/l/cbCait8yG924Lxpr","pdf",928710,1,15,"English","en",105,"# Introduction\n## Data collection challenge\n## Data preprocessing challenge\n## Development challenge\n## Deployment challenge\n## Monitoring challenge","[{\"question\":\"What problem does Edge Impulse address for TinyML development?\",\"answer\":\"It targets fragmented software stacks and heterogeneous deployment hardware that make ML optimizations difficult and reduce portability across embedded targets.\"},{\"question\":\"How does Edge Impulse streamline the TinyML design cycle?\",\"answer\":\"It provides an extensible platform that supports software and hardware optimizations to create portable software stacks for many embedded systems.\"},{\"question\":\"What major challenges in embedded ML does the document list?\",\"answer\":\"It highlights data collection, data preprocessing (including DSP integration), development (dependency matching), deployment (architecture heterogeneity and code portability), and monitoring (lack of unified programmatic MLOps and limited benchmarking tools).\"}]","EDGE IMPULSE - AN MLOPS PLATFORM FOR TINY MACHINE LEARNING | PDF",1785731451,38,{"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},"edge-impulse-an-mlops-platform-for-tiny-machine-learning","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/edge-impulse-an-mlops-platform-for-tiny-machine-learning/120682/",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 problem does Edge Impulse address for TinyML development?","Question",{"text":75,"@type":76},"It targets fragmented software stacks and heterogeneous deployment hardware that make ML optimizations difficult and reduce portability across embedded targets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Edge Impulse streamline the TinyML design cycle?",{"text":80,"@type":76},"It provides an extensible platform that supports software and hardware optimizations to create portable software stacks for many embedded systems.",{"name":82,"@type":73,"acceptedAnswer":83},"What major challenges in embedded ML does the document list?",{"text":84,"@type":76},"It highlights data collection, data preprocessing (including DSP integration), development (dependency matching), deployment (architecture heterogeneity and code portability), and monitoring (lack of unified programmatic MLOps and limited benchmarking tools).","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]