{"id":9415,"date":"2026-03-03T10:31:19","date_gmt":"2026-03-03T02:31:19","guid":{"rendered":"https:\/\/ascendas-asia.com\/?page_id=9415"},"modified":"2026-03-05T15:08:31","modified_gmt":"2026-03-05T07:08:31","slug":"what-is-tinyml","status":"publish","type":"page","link":"https:\/\/ascendas-asia.com\/vi\/resources\/what-is-tinyml\/","title":{"rendered":"What is tinyML?"},"content":{"rendered":"<div data-hs-responsive-table=\"true\" style=\"overflow-x: auto; max-width: 100%; width: 100%; margin-left: auto; margin-right: auto;\">\n<table style=\"width: 100%; border-collapse: collapse; table-layout: fixed; border: 1px solid #99acc2;\">\n<tbody>\n<tr>\n<td style=\"width: 61.9091%; padding: 4px; border-style: hidden; border-width: 0px; vertical-align: top;\">\n<h2><strong>What Is tinyML?<\/strong><\/h2>\n<div>\n<p>Tiny machine learning (tinyML) is a subset of machine learning focused on the deployment of models to microcontrollers and other low-power edge devices. It brings AI to the edge of a networked system, enabling real-time, low-latency, and energy-efficient inference directly on the device without relying on cloud connectivity. Unlike broader Embedded AI, which can encompass powerful edge servers and IoT devices, tinyML targets devices at the smallest end of the spectrum, often running with milliwatt power budgets. Engineers in this area are primarily concerned with optimizing algorithms and models to maintain performance while minimizing power consumption and footprint, enabling intelligent features in the smallest devices and sensors.<\/p>\n<\/div>\n<\/td>\n<td style=\"width: 38.0909%; padding: 4px; border: 0px hidden #ffffff; vertical-align: top;\"><span style=\"color: #ff9902;\"><strong>Additional Resources<\/strong><\/span><span style=\"color: #ff9902;\"><\/span><\/p>\n<ul>\n<li><a href=\"https:\/\/www.mathworks.com\/videos\/how-to-develop-and-deploy-a-neural-network-for-mcus-in-4-steps-1716353717559.html\">How to Develop and Deploy a Neural Network for MCUs in 4 Steps<\/a><\/li>\n<li><a href=\"https:\/\/ascendas-asia.com\/vi\/ai-with-model-based-design\/\">AI with Model-Based Design<\/a><\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div data-hs-responsive-table=\"true\" style=\"overflow-x: auto; max-width: 100%; width: 100%; margin-left: auto; margin-right: auto;\">\n<table style=\"width: 100%; border-collapse: collapse; table-layout: fixed; border: 1px solid #99acc2;\">\n<tbody>\n<tr>\n<td style=\"width: 100%; padding: 4px; border-style: hidden; border-width: 0px; vertical-align: top;\">\n<div>\n<p>Essential stages within the tinyML workflow are:<\/p>\n<ul>\n<li>Model development and training: Training your chosen model using preprocessed data, employing techniques such as transfer learning or data augmentation to achieve the desired accuracy while considering the limitations of the target device.<\/li>\n<li>Model optimization and evaluation: Optimizing the trained model to make it more resource-efficient, employing techniques such as quantization, pruning, projection, and data type conversion to reduce memory and computational requirements without sacrificing significant accuracy.<\/li>\n<li>Deployment: Deploying the optimized model onto the target device, ensuring it can perform real-time inference with low latency.<\/li>\n<li>Testing and validation: Testing and validating the deployed model on the target device using representative data to verify its performance in real-world scenarios and identify any potential issues or limitations.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<div>\n<figure><a href=\"https:\/\/www.mathworks.com\/discovery\/tinyml.html#\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.mathworks.com\/discovery\/tinyml\/_jcr_content\/mainParsys\/columns\/81fe8df9-fe98-467c-82ee-f042e8b13a67\/image.adapt.full.medium.jpg\/1765820957455.jpg\" width=\"510\" height=\"382\" style=\"width: 510px; height: auto; max-width: 100%; margin-left: auto; margin-right: auto; display: block;\" \/><\/a><\/p>\n<div><\/div><figcaption>MATLAB and Simulink support the entire tinyML workflow, enabling design, testing, and deployment of AI-based systems at the edge.<\/p>\n<p>&nbsp;<\/p>\n<\/figcaption><\/figure>\n<\/div>\n<div>\n<figure><a href=\"https:\/\/www.mathworks.com\/discovery\/tinyml.html#\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.mathworks.com\/discovery\/tinyml\/_jcr_content\/mainParsys\/columns\/164816a0-0ae1-467d-9d59-600a6fb545a4\/image.adapt.full.medium.jpg\/1765820957469.jpg\" width=\"580\" height=\"280\" style=\"width: 580px; height: auto; max-width: 100%; margin-left: auto; margin-right: auto; display: block;\" \/><\/a><\/p>\n<div><\/div><figcaption>Automatic code generation from MATLAB and Simulink enables rapid prototyping and deployment of tinyML applications on embedded devices, bridging the gap between theory and practice.<\/p>\n<p>&nbsp;<\/p>\n<\/figcaption><\/figure>\n<\/div>\n<h2><strong>tinyML with MATLAB and Simulink<\/strong><\/h2>\n<p>MATLAB\u00ae provides a high-level programming environment for prototyping and experimenting with machine learning algorithms. Simulink\u00ae offers a block diagram environment for designing and simulating models of systems, facilitating iteration and validation before moving to hardware. The details below describe some capabilities of MATLAB and Simulink that enable the tinyML workflow.<\/p>\n<ul>\n<li>Model Development and Training<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\">To develop and train tinyML networks, you can use MATLAB and Simulink, which offer machine learning and deep learning via apps and a high-level language and block diagram modeling environment. You can <a href=\"https:\/\/www.mathworks.com\/help\/deeplearning\/import-build-deep-neural-networks.html\">import networks<\/a> from TensorFlow\u2122, PyTorch\u00ae, and ONNX with <a href=\"https:\/\/www.mathworks.com\/products\/deep-learning.html\">Deep Learning Toolbox\u2122<\/a> to speed up your network development and training.<\/p>\n<ul>\n<li>Model Optimization<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\">To optimize your machine learning models for resource-constrained edge devices, you can use Deep Learning Toolbox. MATLAB and Simulink include tools for model <a href=\"https:\/\/www.mathworks.com\/help\/deeplearning\/quantization.html\">quantization, projection, pruning<\/a>, and data type conversion that allow you to reduce the memory footprint and computational requirements of your models while maintaining acceptable accuracy. This enables efficient execution on low-power devices without sacrificing the performance of the model.<\/p>\n<ul>\n<li>Code Generation and Deployment<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\">You can generate optimized C\/C++ code from your trained models using <a href=\"https:\/\/www.mathworks.com\/products\/embedded-coder.html\">Embedded Coder\u00ae<\/a>. The generated code can include processor-specific optimizations and device drivers that can be directly deployed on microcontrollers or embedded systems, enabling efficient deployment of tinyML. MathWorks works with its partnered semiconductor companies to support a wide range of popular microcontroller platforms, making it easy to target your specific hardware.<\/p>\n<ul>\n<li>Real-Time Testing and Verification<\/li>\n<\/ul>\n<p style=\"padding-left: 40px;\"><a href=\"https:\/\/www.mathworks.com\/discovery\/hardware-in-the-loop-hil.html\">Hardware-in-the-loop<\/a> (HIL) simulation enables you to simulate and test your tinyML models in real time. This allows you to validate the performance of your models in a virtual real-time environment that represents your physical system before deployment to hardware. MATLAB and Simulink enable integration between simulation and deployment, which helps ensure reliable and accurate results through targeted <a href=\"https:\/\/www.mathworks.com\/hardware-support\/home.html\">hardware support packages<\/a> (HSPs).<\/p>\n<\/div>\n<div>\n<hr \/>\n<div>\n<h2><strong>Examples and How To<\/strong><\/h2>\n<ul>\n<li><a href=\"https:\/\/www.mathworks.com\/videos\/deploying-a-deep-learning-based-state-of-charge-soc-estimation-algorithm-to-nxp-s32k3-microcontrollers-1638200087678.html\">Deploying a Deep Learning\u2013Based State-of-Charge (SOC) Estimation Algorithm to NXP S32K3 Microcontrollers (34:09)<\/a> &#8211; Video<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/company\/technical-articles\/compressing-neural-networks-using-network-projection.html\">Compressing Neural Networks Using Network Projection<\/a> &#8211; Article<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/videos\/deep-network-quantization-and-deployment-using-deep-learning-toolbox-model-quantization-library-1604297928982.html\">Deep Network Quantization and Deployment Using Deep Learning Toolbox Model Quantization Library (5:14)<\/a> &#8211; Video<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/videos\/accelerate-ai-based-software-development-on-aurix-1688133916361.html\">Accelerate AI-Based Software Development on AURIXTM TC4x with Model-Based Design and Optimized Code (34:36)<\/a> &#8211; Video<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/stats\/compress-machine-learning-model-for-memory-limited-hardware.html\">Compress Machine Learning Model for Memory-Limited Hardware<\/a> &#8211; Example<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/deeplearning\/ug\/deploy-compressed-network-to-resource-constrained-devices.html\">Compress Image Classification Network for Deployment to Resource-Constrained Embedded Devices<\/a> &#8211; Example<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/deeplearning\/ug\/predict-soc-using-deep-learning.html\">Predict Battery State of Charge Using Deep Learning<\/a> &#8211; Example<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/videos\/workflow-for-deploying-a-neural-network-to-a-stm32-1693367410706.html\">Workflow for Deploying a Neural Network to a STM32 (11:40)<\/a> &#8211; Video<\/li>\n<\/ul>\n<\/div>\n<hr \/>\n<div>\n<h2><strong>Software Reference<\/strong><\/h2>\n<ul>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/deeplearning\/ref\/deepnetworkquantizer-app.html\">Deep Network Quantizer<\/a> &#8211; Documentation<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/43093-embedded-coder-support-package-for-stmicroelectronics-stm32-processors\">Embedded Coder Support Package for STMicroelectronics STM32 Processors<\/a> &#8211; File Exchange<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/hardware-support\/infineon-aurix.html\">Embedded Coder Support Package for Infineon AURIX TC4x Microcontrollers<\/a> &#8211; Hardware Support<\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/coder\/ug\/generate-generic-cc-code-for-deep-learning-networks.html\">Generate Generic C\/C++ Code for Deep Learning Networks<\/a> &#8211; Documentation<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: center;\"><a class=\"maxbutton-4 maxbutton maxbutton-download-a-free-trial\" target=\"_blank\" rel=\"noopener\" href=\"https:\/\/ascendas-asia.com\/vi\/free-matlab-trial\/\"><span class='mb-text'>Download a FREE Trial<\/span><\/a> <a class=\"maxbutton-1 maxbutton maxbutton-get-quote\" target=\"_blank\" rel=\"noopener\" href=\"https:\/\/ascendas-asia.com\/vi\/company\/#contact-us\"><span class='mb-text'>Request a quote<\/span><\/a><\/p>\n<\/div>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>What Is tinyML? Tiny machine learning (tinyML) is a subset of machine learning focused on the deployment of models to microcontrollers and other low-power edge devices. It brings AI to the edge of a networked system, enabling real-time, low-latency, and energy-efficient inference directly on the device without relying on cloud connectivity. Unlike broader Embedded AI, [&hellip;]<\/p>","protected":false},"author":43,"featured_media":8004,"parent":18,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-9415","page","type-page","status-publish","has-post-thumbnail","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.1 (Yoast SEO v27.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>What is tinyML? - TechSource Systems &amp; Ascendas Systems Group<\/title>\n<meta name=\"description\" content=\"tinyML enables machine learning on low-power devices. Explore how to develop tinyML applications in MATLAB and Simulink.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ascendas-asia.com\/vi\/resources\/what-is-tinyml\/\" \/>\n<meta property=\"og:locale\" content=\"vi_VN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is tinyML?\" \/>\n<meta property=\"og:description\" content=\"tinyML enables machine learning on low-power devices. Explore how to develop tinyML applications in MATLAB and Simulink.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ascendas-asia.com\/vi\/resources\/what-is-tinyml\/\" \/>\n<meta property=\"og:site_name\" content=\"TechSource Systems &amp; Ascendas Systems Group\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/techsourcesystems\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-05T07:08:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2024\/10\/banner-5.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1494\" \/>\n\t<meta property=\"og:image:height\" content=\"368\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u01af\u1edbc t\u00ednh th\u1eddi gian \u0111\u1ecdc\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 ph\u00fat\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/\",\"url\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/\",\"name\":\"What is tinyML? - TechSource Systems &amp; Ascendas Systems Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/ascendas-asia.com\\\/wp-content\\\/uploads\\\/2024\\\/10\\\/banner-5.png\",\"datePublished\":\"2026-03-03T02:31:19+00:00\",\"dateModified\":\"2026-03-05T07:08:31+00:00\",\"description\":\"tinyML enables machine learning on low-power devices. Explore how to develop tinyML applications in MATLAB and Simulink.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/#breadcrumb\"},\"inLanguage\":\"vi\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"vi\",\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/#primaryimage\",\"url\":\"https:\\\/\\\/ascendas-asia.com\\\/wp-content\\\/uploads\\\/2024\\\/10\\\/banner-5.png\",\"contentUrl\":\"https:\\\/\\\/ascendas-asia.com\\\/wp-content\\\/uploads\\\/2024\\\/10\\\/banner-5.png\",\"width\":1494,\"height\":368},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/what-is-tinyml\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/ascendas-asia.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Resources\",\"item\":\"https:\\\/\\\/ascendas-asia.com\\\/resources\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"What is tinyML?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/#website\",\"url\":\"https:\\\/\\\/ascendas-asia.com\\\/\",\"name\":\"TechSource Systems & Ascendas Systems Group | MathWorks Authorized Reseller\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/ascendas-asia.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"vi\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/#organization\",\"name\":\"TechSource Systems & Ascendas Systems Group\",\"url\":\"https:\\\/\\\/ascendas-asia.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"vi\",\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/ascendas-asia.com\\\/wp-content\\\/uploads\\\/2021\\\/12\\\/logo.jpg\",\"contentUrl\":\"https:\\\/\\\/ascendas-asia.com\\\/wp-content\\\/uploads\\\/2021\\\/12\\\/logo.jpg\",\"width\":825,\"height\":131,\"caption\":\"TechSource Systems & Ascendas Systems Group\"},\"image\":{\"@id\":\"https:\\\/\\\/ascendas-asia.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/techsourcesystems\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/techsource-systems\\\/\",\"https:\\\/\\\/www.youtube.com\\\/c\\\/TechSourceSystems\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"What is tinyML? - TechSource Systems &amp; Ascendas Systems Group","description":"tinyML enables machine learning on low-power devices. Explore how to develop tinyML applications in MATLAB and Simulink.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/ascendas-asia.com\/vi\/resources\/what-is-tinyml\/","og_locale":"vi_VN","og_type":"article","og_title":"What is tinyML?","og_description":"tinyML enables machine learning on low-power devices. Explore how to develop tinyML applications in MATLAB and Simulink.","og_url":"https:\/\/ascendas-asia.com\/vi\/resources\/what-is-tinyml\/","og_site_name":"TechSource Systems &amp; Ascendas Systems Group","article_publisher":"https:\/\/www.facebook.com\/techsourcesystems","article_modified_time":"2026-03-05T07:08:31+00:00","og_image":[{"width":1494,"height":368,"url":"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2024\/10\/banner-5.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"\u01af\u1edbc t\u00ednh th\u1eddi gian \u0111\u1ecdc":"4 ph\u00fat"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/","url":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/","name":"What is tinyML? - TechSource Systems &amp; Ascendas Systems Group","isPartOf":{"@id":"https:\/\/ascendas-asia.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/#primaryimage"},"image":{"@id":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/#primaryimage"},"thumbnailUrl":"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2024\/10\/banner-5.png","datePublished":"2026-03-03T02:31:19+00:00","dateModified":"2026-03-05T07:08:31+00:00","description":"tinyML enables machine learning on low-power devices. Explore how to develop tinyML applications in MATLAB and Simulink.","breadcrumb":{"@id":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/#breadcrumb"},"inLanguage":"vi","potentialAction":[{"@type":"ReadAction","target":["https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/"]}]},{"@type":"ImageObject","inLanguage":"vi","@id":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/#primaryimage","url":"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2024\/10\/banner-5.png","contentUrl":"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2024\/10\/banner-5.png","width":1494,"height":368},{"@type":"BreadcrumbList","@id":"https:\/\/ascendas-asia.com\/resources\/what-is-tinyml\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/ascendas-asia.com\/"},{"@type":"ListItem","position":2,"name":"Resources","item":"https:\/\/ascendas-asia.com\/resources\/"},{"@type":"ListItem","position":3,"name":"What is tinyML?"}]},{"@type":"WebSite","@id":"https:\/\/ascendas-asia.com\/#website","url":"https:\/\/ascendas-asia.com\/","name":"TechSource Systems & Ascendas Systems Group | MathWorks Authorized Reseller","description":"","publisher":{"@id":"https:\/\/ascendas-asia.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/ascendas-asia.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"vi"},{"@type":"Organization","@id":"https:\/\/ascendas-asia.com\/#organization","name":"TechSource Systems & Ascendas Systems Group","url":"https:\/\/ascendas-asia.com\/","logo":{"@type":"ImageObject","inLanguage":"vi","@id":"https:\/\/ascendas-asia.com\/#\/schema\/logo\/image\/","url":"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2021\/12\/logo.jpg","contentUrl":"https:\/\/ascendas-asia.com\/wp-content\/uploads\/2021\/12\/logo.jpg","width":825,"height":131,"caption":"TechSource Systems & Ascendas Systems Group"},"image":{"@id":"https:\/\/ascendas-asia.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/techsourcesystems","https:\/\/www.linkedin.com\/company\/techsource-systems\/","https:\/\/www.youtube.com\/c\/TechSourceSystems"]}]}},"_links":{"self":[{"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/pages\/9415","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/users\/43"}],"replies":[{"embeddable":true,"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/comments?post=9415"}],"version-history":[{"count":15,"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/pages\/9415\/revisions"}],"predecessor-version":[{"id":9450,"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/pages\/9415\/revisions\/9450"}],"up":[{"embeddable":true,"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/pages\/18"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/media\/8004"}],"wp:attachment":[{"href":"https:\/\/ascendas-asia.com\/vi\/wp-json\/wp\/v2\/media?parent=9415"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}