{"id":25526056,"date":"2022-05-29T22:39:44","date_gmt":"2022-05-29T17:09:44","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25526056"},"modified":"2022-05-29T22:39:44","modified_gmt":"2022-05-29T17:09:44","slug":"machine-learning-on-embedded-devices","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/","title":{"rendered":"Machine Learning On Embedded Devices"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69ea1c6f20fbf\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69ea1c6f20fbf\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/#How_machine_learning_works\" >How machine learning works<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/#Benefits_of_combining_ML_and_embedded_systems\" >Benefits of combining ML and embedded systems<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/#Few_devices_that_indicate_a_bright_future_for_ML_on_embedded_systems\" >Few devices that indicate a bright future for ML on embedded systems<\/a><\/li><\/ul><\/nav><\/div>\n<p>Machine learning in embedded systems allows the use of that data in automated business processes to make more educated predictions. Running machine learning models on embedded devices is generally known as embedded machine learning.\u00a0\u00a0Machine learning\u00a0leverages a large amount of historic data to enable electronic systems to learn autonomously and use that knowledge for analysis, predictions, and decision making. Devices such as these can fulfill many tasks in the industry. Such devices allow machine learning algorithms on low-power devices like microcontrollers.<\/p>\n<p style=\"text-align: center\"><a href=\"https:\/\/entri.sng.link\/Bcofz\/yeoy\/ojyv\" target=\"_blank\" rel=\"noopener\"><strong>To know more about Python, Download Entri App!<\/strong><\/a><\/p>\n<p>Machine learning in embedded devices has many benefits. It eliminates the need for transferring and storing data on cloud servers, which reduces data breaches and privacy leaks involved in transferring data. It also reduces the theft of Intellectual property, personal data, and business secrets. The execution of\u00a0ML models\u00a0eliminates the need of transferring data to a cloud server, this economizes the bandwidth and network resources. Using embedded devices that run on ML-based models is also sustainable as it has a far lower carbon footprint. The low carbon footprint is attributed to the fact that microcontrollers used in the device are power efficient. Embedded systems are much more efficient than cloud-based systems. This is due to the fact that there is no need to transfer a large amount of data to the cloud which contributes to significant network latency.<\/p>\n<p>An\u00a0<strong>embedded system<\/strong>\u00a0is hardware and software designed to perform a dedicated function. The software is \u201cembedded\u201d directly into the hardware during development. Some embedded systems are independent, while others work as part of a more extensive system or network. Embedded systems vary in complexity, from simple microprocessors to multicore processors. More complicated systems include graphical user interfaces and other connected peripherals.<\/p>\n<p style=\"text-align: center\"><strong><a href=\"https:\/\/bit.ly\/3MWTnuS\" target=\"_blank\" rel=\"noopener\">Grab Latest Study Materials! Register Here!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_machine_learning_works\"><\/span><strong>How machine learning works<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Machine learning (ML)\u00a0is a category of\u00a0artificial intelligence (AI)\u00a0that learns from data. ML then applies these insights without humans. Using statistics, machine learning can identify patterns within large datasets or Big Data. The software scope is no longer limited because ML algorithms can develop new processes on-the-fly.<\/p>\n<p>However, the quality of ML insights varies. This depends on the data\u2019s structure, the algorithm\u2019s technique, and more. For example, supervised algorithms use pre-labelled datasets during training. Unsupervised algorithms use unclassified data. Input data quality is an essential factor for achieving accurate and high-quality ML outputs.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Benefits_of_combining_ML_and_embedded_systems\"><\/span><strong>Benefits of combining ML and embedded systems<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>By combining ML with embedded systems, companies can gather data, analyse it, and make predictions. This process can improve their hardware and business-critical systems\u2019 performance. With deep learning, companies can achieve a level of embedded systems intelligence that wasn\u2019t possible before.<\/p>\n<p>For example, image and speech recognition has been a challenge for computers. In the past, software couldn\u2019t analyse enough data to learn. The sheer amount of variations possible couldn\u2019t be accounted for. Cheaper and more powerful hardware enables embedded systems to replicate human-like tasks.<\/p>\n<p>ML is often used to learn more about sensor or device behaviour. This is useful for preventative maintenance, anomaly detection, and improved efficiency. Companies can identify patterns for device decay that engineers may not be aware of. ML can reduce the cost of embedded systems while overcoming their constraints.<\/p>\n<p>The innovation market has been hot since the inception of embedded technology and many products have captured the attention of enthusiasts.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Few_devices_that_indicate_a_bright_future_for_ML_on_embedded_systems\"><\/span><strong>Few devices that indicate a bright future for ML on embedded systems<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><strong>NVIDIA Jetson Xavier NX-based Industrial AI smart camera<\/strong><\/h3>\n<p>Adlink technology has come up with the industry\u2019s first industrial smart camera that comes with NVIDIA\u2019s Jetson Xavier NX. This camera is a high-performance and small form factor camera. It will open the door to AI innovation in manufacturing, logistics, healthcare, agriculture, and many other business sectors. The product is about ten times more efficient than its predecessor. It is a hassle-free, compact, reliable, and powerful product for edge AI applications and also the best match for AI software providers.<\/p>\n<p style=\"text-align: center\"><a href=\"https:\/\/entri.sng.link\/Bcofz\/yeoy\/ojyv\" target=\"_blank\" rel=\"noopener\"><strong>To know more about Python, Download Entri App!<\/strong><\/a><\/p>\n<h3><strong>NVIDIA Jetson TX2 NX<\/strong><\/h3>\n<p>NVIDIA\u00a0has unveiled another single-board computer that embodies AI performance for entry-level embedded and edge products. It is faster than its predecessor Jetson Nano and shares form factor and pin compatibility with it. Its speed of development along with a unique combination of form factor, performance, and power advantage makes it the ideal choice as an AI product platform.<\/p>\n<h3><strong>Google\u2019s Edge TPU<\/strong><\/h3>\n<p>Google has purposely built this to run AI at the edge. Edge TPU complements cloud TPU and\u00a0Google\u00a0cloud services to provide end-to-end, cloud-to-edge, hardware+software infrastructure for facilitating the deployment of customer\u2019s AI-based solutions. It has a low carbon footprint and consumes very little power. Users can leverage the Edge TPU to deploy high-quality machine learning inference at the edge, using the coral platform that is built for ML at the edge.<\/p>\n<h3><strong>Intel Movidius<\/strong><\/h3>\n<p>Intel Movidius VPUs enable demanding computer vision and edge AI workloads with extreme efficiency. It minimizes data movement by coupling parallel programmable compute with workload-specific hardware acceleration in a novel technological architecture. It allows intelligent cameras, edge servers, and AI appliances with deep neural networks and computer vision-based applications that can be of great advantage in industrial automation.<\/p>\n<p style=\"text-align: center\"><strong><a href=\"https:\/\/bit.ly\/3MWTnuS\" target=\"_blank\" rel=\"noopener\">Grab Latest Study Materials! Register Here!<\/a><\/strong><\/p>\n<h3><strong>Apple\u2019s Xnor<\/strong><\/h3>\n<p>Apple has been synonymous with innovation and introduced customised AI chips that have been mounting AI workloads on their flagship devices. To further its efforts, Apple acquired Xnor.ai for a massive amount of $200 million. It has introduced a tiny device that works like a solar-powered calculator and runs state-of-the-art object recognition. It can operate independently of the cloud for tasks such as facial recognition, natural language processing while safeguarding the privacy of users.<\/p>\n<p>The tech giants have increased their spending on developing edge and tiny ML systems.\u00a0 There is even a massive search for talent ranging from embedded software engineers to embedded hardware engineers.<\/p>\n<p style=\"text-align: center\"><a href=\"https:\/\/entri.sng.link\/Bcofz\/yeoy\/ojyv\" target=\"_blank\" rel=\"noopener\"><strong>To know more about Python, Download Entri App!<\/strong><\/a><\/p>\n<p><strong>Why is it important to choose Entri?<\/strong><\/p>\n<ul>\n<li>Excellent online platform for all the Competitive Exams.<\/li>\n<li>Provides updated materials created by the Entri Experts.<\/li>\n<li>Entri provides a best platform with full- length mock tests including previous year question papers.<\/li>\n<li>You can download the app for free and join the required classes.<\/li>\n<li>Entri wishes you all the best for your examinations and future endeavours.<\/li>\n<\/ul>\n<p><strong>\u201cYOU DON\u2019T HAVE TO BE GREAT TO START, BUT YOU HAVE TO START TO BE GREAT.\u201d<\/strong><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning in embedded systems allows the use of that data in automated business processes to make more educated predictions. Running machine learning models on embedded devices is generally known as embedded machine learning.\u00a0\u00a0Machine learning\u00a0leverages a large amount of historic data to enable electronic systems to learn autonomously and use that knowledge for analysis, predictions, [&hellip;]<\/p>\n","protected":false},"author":55,"featured_media":25526061,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,558],"tags":[],"class_list":["post-25526056","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-general-knowledge"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning On Embedded Devices - Entri Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning On Embedded Devices - Entri Blog\" \/>\n<meta property=\"og:description\" content=\"Machine learning in embedded systems allows the use of that data in automated business processes to make more educated predictions. Running machine learning models on embedded devices is generally known as embedded machine learning.\u00a0\u00a0Machine learning\u00a0leverages a large amount of historic data to enable electronic systems to learn autonomously and use that knowledge for analysis, predictions, [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/\" \/>\n<meta property=\"og:site_name\" content=\"Entri Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/entri.me\/\" \/>\n<meta property=\"article:published_time\" content=\"2022-05-29T17:09:44+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/05\/Machine-Learning-On-Embedded-Devices.png\" \/>\n\t<meta property=\"og:image:width\" content=\"820\" \/>\n\t<meta property=\"og:image:height\" content=\"615\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Ayesha Surayya\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@entri_app\" \/>\n<meta name=\"twitter:site\" content=\"@entri_app\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ayesha Surayya\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/\"},\"author\":{\"name\":\"Ayesha Surayya\",\"@id\":\"https:\/\/entri.app\/blog\/#\/schema\/person\/568cc9d6e77fd5d01033b61c88343097\"},\"headline\":\"Machine Learning On Embedded Devices\",\"datePublished\":\"2022-05-29T17:09:44+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/\"},\"wordCount\":1081,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/entri.app\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/05\/Machine-Learning-On-Embedded-Devices.png\",\"articleSection\":[\"Articles\",\"General Knowledge\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/\",\"url\":\"https:\/\/entri.app\/blog\/machine-learning-on-embedded-devices\/\",\"name\":\"Machine Learning On Embedded Devices - 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