{"id":1352,"date":"2026-01-22T08:39:05","date_gmt":"2026-01-21T23:39:05","guid":{"rendered":"https:\/\/rtlearner.com\/?p=1352"},"modified":"2026-02-27T10:08:15","modified_gmt":"2026-02-27T01:08:15","slug":"ai-architecture-14-dataflow-taxonomy-ws-os-rs","status":"publish","type":"post","link":"https:\/\/rtlearner.com\/en\/ai-architecture-14-dataflow-taxonomy-ws-os-rs\/","title":{"rendered":"AI Architecture 14. Dataflow Taxonomy: TPU vs Output Stationary vs Row Stationary"},"content":{"rendered":"<p><a href=\"https:\/\/rtlearner.com\/en\/ai-architecture-13-roofline-model-analysis\/\">In the previous post<\/a>, we quantitatively confirmed that hardware performance limits are often determined by memory bandwidth. From a system architect's perspective, this fact carries a more significant implication: <strong>the energy cost of moving data to the computation unit is significantly higher than the cost of performing the actual computation<\/strong>.<\/p>\n\n\n\n<p>Research indicates that fetching data from DRAM consumes approximately <strong>200 times<\/strong> more energy than fetching it from a Register File (RF). Therefore, the core of high-performance NPU design is not merely increasing the number of MAC units, but designing a strategy to keep data in on-chip memory or registers as long as possible for Reuse.<\/p>\n\n\n\n<p>This strategy of <strong>Spatio-temporal Mapping<\/strong>is called Dataflow. Depending on which data type is kept Stationary, the characteristics of the NPU architecture change completely. This article provides an in-depth analysis of the three primary Dataflows: <strong>Weight Stationary (WS), Output Stationary (OS), and Row Stationary (RS)<\/strong>.<\/p>\n\n\n<style>.kb-table-of-content-nav.kb-table-of-content-id1352_b3e936-9e .kb-table-of-content-wrap{padding-top:var(--global-kb-spacing-sm, 1.5rem);padding-right:var(--global-kb-spacing-sm, 1.5rem);padding-bottom:var(--global-kb-spacing-sm, 1.5rem);padding-left:var(--global-kb-spacing-sm, 1.5rem);box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}.kb-table-of-content-nav.kb-table-of-content-id1352_b3e936-9e .kb-table-of-contents-title-wrap{padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}.kb-table-of-content-nav.kb-table-of-content-id1352_b3e936-9e .kb-table-of-contents-title{font-weight:regular;font-style:normal;}.kb-table-of-content-nav.kb-table-of-content-id1352_b3e936-9e .kb-table-of-content-wrap .kb-table-of-content-list{font-weight:regular;font-style:normal;margin-top:var(--global-kb-spacing-sm, 1.5rem);margin-right:0px;margin-bottom:0px;margin-left:0px;}@media all and (max-width: 767px){.kb-table-of-content-nav.kb-table-of-content-id1352_b3e936-9e .kb-table-of-contents-title{font-size:var(--global-kb-font-size-md, 1.25rem);}.kb-table-of-content-nav.kb-table-of-content-id1352_b3e936-9e .kb-table-of-content-wrap .kb-table-of-content-list{font-size:var(--global-kb-font-size-sm, 0.9rem);}}<\/style>\n\n<style>.kadence-column1352_bfaf28-47 > .kt-inside-inner-col{box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}.kadence-column1352_bfaf28-47 > .kt-inside-inner-col,.kadence-column1352_bfaf28-47 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column1352_bfaf28-47 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column1352_bfaf28-47 > .kt-inside-inner-col{flex-direction:column;}.kadence-column1352_bfaf28-47 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column1352_bfaf28-47 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column1352_bfaf28-47{position:relative;}@media all and (max-width: 1024px){.kadence-column1352_bfaf28-47 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column1352_bfaf28-47 > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column1352_bfaf28-47\"><div class=\"kt-inside-inner-col\">\n<p><strong>Related articles<\/strong><\/p>\n\n\n\n<p>\u2705<a href=\"https:\/\/rtlearner.com\/en\/ai-architecture-15-systolic-array-architecture\/\" data-type=\"post\" data-id=\"1364\">AI Architecture 15. The Heart of Systolic Array<\/a><\/p>\n\n\n\n<p>\u2705<a href=\"https:\/\/rtlearner.com\/en\/ai-architecture-16-npu-optimization-memory-hierarchy\/\" data-type=\"post\" data-id=\"1383\">AI Architecture 16. Memory Hierarchy: Minimize Data Movement Costs<\/a><\/p>\n<\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">1. Weight Stationary (WS): Fix the Weights<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Concept &amp; Mechanism<\/h3>\n\n\n\n<p><strong>Weight Stationary<\/strong>fixes the Weights (Filters), a key element of deep learning operations, in the registers inside the PE (Processing Element), while allowing Inputs (Input Activations) and Partial Sums to move.<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Weights are pre-loaded into PE registers and held stationary.<\/li>\n\n\n\n<li>Input data (Input Feature Maps) are broadcasted or flowed through the array.<\/li>\n\n\n\n<li>Calculated results (Partial Sums) move to adjacent PEs to be accumulated.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Representative Architecture<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Google Tensor Processing Unit (TPU) v1:<\/strong> Implemented WS using a Systolic Array structure.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Pros<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Efficiency for CNN\/LLM:<\/strong> Filters in CNNs or weight matrices in LLMs are reused repeatedly across multiple inputs once loaded. WS maximizes this property to reduce memory access costs.<\/li>\n\n\n\n<li><strong>Simplified Control:<\/strong> Since weights are static after loading, the control logic for flowing inputs is relatively simple.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Cons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Partial Sum Movement Cost:<\/strong> Partial sums must continuously move between PEs until accumulation is complete, consuming interconnect bandwidth.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">2. Output Stationary (OS): Fix the Results<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Concept &amp; Mechanism<\/h3>\n\n\n\n<p><strong>Output Stationary<\/strong>fixes the Partial Sums required to generate the final Output Activation in the PE's internal registers.<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Each PE is responsible for one Output Pixel.<\/li>\n\n\n\n<li>Inputs and Weights required to compute this output are streamed to the PE.<\/li>\n\n\n\n<li>Partial sums do not leave the PE until the accumulation is finished.<\/li>\n\n\n\n<li>Only the final result is written out to memory.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Representative Architecture<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>ShiDianNao:<\/strong> An early NPU architecture optimized for tasks with high operation density within specific windows, such as image processing.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Pros<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Minimize Partial Sum Memory Access:<\/strong> Since the read\/write process for partial sums occurs only within registers, global buffer traffic for partial sums is drastically reduced. (This is significant as partial sum data often requires higher bit-width precision).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Cons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Input\/Weight Bandwidth:<\/strong> Inputs and weights must be broadcasted or unicasted every cycle, potentially increasing the global bandwidth requirement to supply this data.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">3. Row Stationary (RS): Maximizing 2D Reuse<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Concept &amp; Mechanism<\/h3>\n\n\n\n<p><strong>Row Stationary<\/strong>is the core technology of the <strong>Eyeriss<\/strong> architecture proposed by MIT. Unlike WS or OS which fix a single data type, RS is a composite method designed to <strong>maximize reuse for Inputs, Weights, and Partial Sums simultaneously<\/strong>.<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Due to the nature of Convolution, 2D planar data is processed in a sliding window manner.<\/li>\n\n\n\n<li>RS maps data to PEs in units of 1D Rows.<\/li>\n\n\n\n<li>By increasing the RF (Register File) capacity per PE, it fixes a Row of Weights, flows a Row of Inputs, and manages the Row of Partial Sums internally.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Representative Architecture<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>MIT Eyeriss:<\/strong> An edge NPU pursuing extreme energy efficiency.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Pros<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Overall Energy Optimization:<\/strong> Achieves balanced reuse across Inputs, Weights, and Outputs without biasing towards a specific data type, thereby minimizing total system energy.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Cons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Complex Control Logic:<\/strong> The data mapping scheme is highly complex, increasing the difficulty of compiler and hardware controller design.<\/li>\n\n\n\n<li><strong>Increased PE Area:<\/strong> Requires larger local memory (SRAM\/RF) per PE to store the complex data sets.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">4. Comparison Analysis &amp; Conclusion<\/h2>\n\n\n<style>.kb-table-container1352_5abac8-5f{font-size:var(--global-kb-font-size-sm, 0.9rem);max-height:899px;overflow-x:auto;}.kb-table-container .kb-table1352_5abac8-5f{overflow-x:scroll;}.kb-table-container .kb-table1352_5abac8-5f th{padding-top:var(--global-kb-spacing-xxs, 0.5rem);padding-right:var(--global-kb-spacing-xxs, 0.5rem);padding-bottom:var(--global-kb-spacing-xxs, 0.5rem);padding-left:var(--global-kb-spacing-xxs, 0.5rem);text-align:center;}.kb-table-container .kb-table1352_5abac8-5f caption{text-align:center;}.kb-table-container .kb-table1352_5abac8-5f td{padding-top:var(--global-kb-spacing-xxs, 0.5rem);padding-right:var(--global-kb-spacing-xxs, 0.5rem);padding-bottom:var(--global-kb-spacing-xxs, 0.5rem);padding-left:var(--global-kb-spacing-xxs, 0.5rem);text-align:left;}@media all and (max-width: 767px){.kb-table-container1352_5abac8-5f{font-size:var(--global-kb-font-size-sm, 0.9rem);}}<\/style><div class=\"kb-table-container kb-table-container1352_5abac8-5f wp-block-kadence-table\"><table class=\"kb-table kb-table1352_5abac8-5f\">\n<tr class=\"kb-table-row kb-table-row1352_30947c-ca\">\n<th class=\"kb-table-data kb-table-data1352_df1c85-a8\">\n\n<p><strong>Characteristic<\/strong><\/p>\n\n<\/th>\n\n<th class=\"kb-table-data kb-table-data1352_9e8b61-f1\">\n\n<p><strong>Weight Stationary (WS)<\/strong><\/p>\n\n<\/th>\n\n<th class=\"kb-table-data kb-table-data1352_74ec69-52\">\n\n<p><strong>Output Stationary (OS)<\/strong><\/p>\n\n<\/th>\n\n<th class=\"kb-table-data kb-table-data1352_c72ba8-5d\">\n\n<p><strong>Row Stationary (RS)<\/strong><\/p>\n\n<\/th>\n<\/tr>\n\n<tr class=\"kb-table-row kb-table-row1352_a23ff4-25\">\n<th class=\"kb-table-data kb-table-data1352_a29ed2-94\">\n\n<p><strong>Stationary Data<\/strong><\/p>\n\n<\/th>\n\n<td class=\"kb-table-data kb-table-data1352_02cc3a-ac\">\n\n<p>Weights (Filters)<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_1c0b34-37\">\n\n<p>Partial Sums (Outputs)<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_6d4956-08\">\n\n<p>Row of Weights &amp; Inputs<\/p>\n\n<\/td>\n<\/tr>\n\n<tr class=\"kb-table-row kb-table-row1352_e65108-ba\">\n<th class=\"kb-table-data kb-table-data1352_612155-c9\">\n\n<p><strong>Moving Data<\/strong><\/p>\n\n<\/th>\n\n<td class=\"kb-table-data kb-table-data1352_7c05ac-eb\">\n\n<p>Inputs, Partial Sums<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_faad8d-7a\">\n\n<p>Inputs, Weights<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_643514-d3\">\n\n<p>Inputs (Diagonal), Psums<\/p>\n\n<\/td>\n<\/tr>\n\n<tr class=\"kb-table-row kb-table-row1352_8dc4c5-7a\">\n<th class=\"kb-table-data kb-table-data1352_546f7c-ef\">\n\n<p><strong>Optimization Goal<\/strong><\/p>\n\n<\/th>\n\n<td class=\"kb-table-data kb-table-data1352_82d79b-a6\">\n\n<p>Min. Weight Reads<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_9ec72c-f3\">\n\n<p>Min. Psum R\/W<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_5c7ddc-1b\">\n\n<p>Min. Total Data Movement<\/p>\n\n<\/td>\n<\/tr>\n\n<tr class=\"kb-table-row kb-table-row1352_451eed-43\">\n<th class=\"kb-table-data kb-table-data1352_cac23f-f7\">\n\n<p><strong>Suitable Models<\/strong><\/p>\n\n<\/th>\n\n<td class=\"kb-table-data kb-table-data1352_6bbf8c-17\">\n\n<p>Large CNNs, LLMs (Batch\u2191)<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_487e62-a0\">\n\n<p>Depthwise Conv, MLP<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_c14ba5-5f\">\n\n<p>General CNN (Mobile\/Edge)<\/p>\n\n<\/td>\n<\/tr>\n\n<tr class=\"kb-table-row kb-table-row1352_a25ad3-9b\">\n<th class=\"kb-table-data kb-table-data1352_bde87f-79\">\n\n<p><strong>Examples<\/strong><\/p>\n\n<\/th>\n\n<td class=\"kb-table-data kb-table-data1352_88688b-93\">\n\n<p>Google TPU, NVDLA<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_f7cbd6-0b\">\n\n<p>ShiDianNao<\/p>\n\n<\/td>\n\n<td class=\"kb-table-data kb-table-data1352_b6f620-75\">\n\n<p>MIT Eyeriss<\/p>\n\n<\/td>\n<\/tr>\n<\/table><\/div>\n\n<style>.kb-image1352_c1ef08-f0.kb-image-is-ratio-size, .kb-image1352_c1ef08-f0 .kb-image-is-ratio-size{max-width:700px;width:100%;}.wp-block-kadence-column > .kt-inside-inner-col > .kb-image1352_c1ef08-f0.kb-image-is-ratio-size, .wp-block-kadence-column > .kt-inside-inner-col > .kb-image1352_c1ef08-f0 .kb-image-is-ratio-size{align-self:unset;}.kb-image1352_c1ef08-f0 figure{max-width:700px;}.kb-image1352_c1ef08-f0 .image-is-svg, .kb-image1352_c1ef08-f0 .image-is-svg img{width:100%;}.kb-image1352_c1ef08-f0 .kb-image-has-overlay:after{opacity:0.3;}@media all and (max-width: 767px){.kb-image1352_c1ef08-f0.kb-image-is-ratio-size, .kb-image1352_c1ef08-f0 .kb-image-is-ratio-size{max-width:290px;width:100%;}.kb-image1352_c1ef08-f0 figure{max-width:290px;}}<\/style>\n<div class=\"wp-block-kadence-image kb-image1352_c1ef08-f0\"><figure class=\"aligncenter size-full\"><img data-dominant-color=\"cad3da\" data-has-transparency=\"false\" style=\"--dominant-color: #cad3da;\" loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/rtlearner.com\/wp-content\/uploads\/2026\/01\/image-3-10.jpg\" alt=\"Dataflow comparison\" class=\"kb-img wp-image-1360 not-transparent\" srcset=\"https:\/\/rtlearner.com\/wp-content\/uploads\/2026\/01\/image-3-10.jpg 1024w, https:\/\/rtlearner.com\/wp-content\/uploads\/2026\/01\/image-3-10-300x164.jpg 300w, https:\/\/rtlearner.com\/wp-content\/uploads\/2026\/01\/image-3-10-768x419.jpg 768w, https:\/\/rtlearner.com\/wp-content\/uploads\/2026\/01\/image-3-10-18x10.jpg 18w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption>Dataflow comparison<\/figcaption><\/figure><\/div>\n\n\n\n<p><strong>In conclusion, the superior Dataflow is determined by the characteristics of the 'Workload'.<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"translation-block\">WS is advantageous for server-grade inference with large batch sizes and high filter reuse.<\/li>\n\n\n\n<li class=\"translation-block\">OS can be beneficial when image sizes are large, channels are few, or partial sum data size is substantial.<\/li>\n\n\n\n<li class=\"translation-block\">RS is preferred in mobile\/edge environments with strict power constraints, despite the design complexity, due to its highest energy efficiency.<\/li>\n<\/ul>\n\n\n\n<p class=\"translation-block\">Modern high-performance NPUs (e.g., NVIDIA Tensor Cores, Google TPU v4) are evolving to not be fixed to a single dataflow, but to reconfigure or mix dataflows flexibly according to layer characteristics (Conv vs. FC, Kernel Size, etc.).<\/p>\n\n\n<style>.kadence-column1352_dd6d7a-be > .kt-inside-inner-col{box-shadow:0px 0px 14px 0px rgba(0, 0, 0, 0.2);}.kadence-column1352_dd6d7a-be > .kt-inside-inner-col,.kadence-column1352_dd6d7a-be > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column1352_dd6d7a-be > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column1352_dd6d7a-be > .kt-inside-inner-col{flex-direction:column;}.kadence-column1352_dd6d7a-be > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column1352_dd6d7a-be > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column1352_dd6d7a-be{position:relative;}@media all and (max-width: 1024px){.kadence-column1352_dd6d7a-be > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}@media all and (max-width: 767px){.kadence-column1352_dd6d7a-be > .kt-inside-inner-col{flex-direction:column;justify-content:center;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column1352_dd6d7a-be\"><div class=\"kt-inside-inner-col\">\n<p><strong>Related articles<\/strong><\/p>\n\n\n\n<p>\u2705<a href=\"https:\/\/rtlearner.com\/en\/ai-architecture-15-systolic-array-architecture\/\" data-type=\"post\" data-id=\"1364\">AI Architecture 15. The Heart of Systolic Array<\/a><\/p>\n\n\n\n<p>\u2705<a href=\"https:\/\/rtlearner.com\/en\/ai-architecture-16-npu-optimization-memory-hierarchy\/\" data-type=\"post\" data-id=\"1383\">AI Architecture 16. Memory Hierarchy: Minimize Data Movement Costs<\/a><\/p>\n<\/div><\/div>\n\n\n\n<p>References: <em><a href=\"https:\/\/arxiv.org\/abs\/1703.09039\" target=\"_blank\" rel=\"noopener\">Efficient Processing of Deep Neural Networks: A Tutorial and Survey<\/a><\/em><\/p>","protected":false},"excerpt":{"rendered":"<p>In the previous post, we quantitatively confirmed that hardware performance limits ...<\/p>","protected":false},"author":1,"featured_media":1360,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kadence_starter_templates_imported_post":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[116],"tags":[117,118],"class_list":["post-1352","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-and-hw-fundamentals","tag-ai","tag-architecture"],"_links":{"self":[{"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/posts\/1352","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/comments?post=1352"}],"version-history":[{"count":8,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/posts\/1352\/revisions"}],"predecessor-version":[{"id":1417,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/posts\/1352\/revisions\/1417"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/media\/1360"}],"wp:attachment":[{"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/media?parent=1352"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/categories?post=1352"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rtlearner.com\/en\/wp-json\/wp\/v2\/tags?post=1352"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- This website is optimized by Airlift. Learn more: https://airlift.net. Template:. Learn more: https://airlift.net. Template: 69b92da9d36f73cd2808d6e8. Config Timestamp: 2026-03-17 10:32:09 UTC, Cached Timestamp: 2026-05-09 16:07:33 UTC -->