{"id":7,"date":"2022-06-30T20:39:33","date_gmt":"2022-06-30T20:39:33","guid":{"rendered":"https:\/\/projetos.lacom.ag\/Celera\/?page_id=7"},"modified":"2022-07-19T17:59:26","modified_gmt":"2022-07-19T17:59:26","slug":"home","status":"publish","type":"page","link":"https:\/\/celera.ai\/","title":{"rendered":"Home"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"7\" data-post-id=\"7\" data-obj-id=\"7\" class=\"elementor elementor-7 dce-elementor-post-7\">\n\t\t\t\t\t\t<section data-dce-background-color=\"#2B53BC\" class=\"elementor-section elementor-top-section elementor-element elementor-element-1d4cfd3 elementor-section-height-min-height elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"1d4cfd3\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;video&quot;,&quot;background_video_link&quot;:&quot;https:\\\/\\\/celera.ai\\\/wp-content\\\/uploads\\\/2022\\\/07\\\/20220708_Celera.m4v&quot;,&quot;background_play_once&quot;:&quot;yes&quot;,&quot;background_play_on_mobile&quot;:&quot;yes&quot;}\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-background-video-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<video class=\"elementor-background-video-hosted elementor-html5-video\" autoplay muted playsinline><\/video>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c46b376\" data-id=\"c46b376\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a34ed6e elementor-widget elementor-widget-spacer\" data-id=\"a34ed6e\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\n.elementor-column .elementor-spacer-inner{height:var(--spacer-size)}.e-con{--container-widget-width:100%}.e-con-inner>.elementor-widget-spacer,.e-con>.elementor-widget-spacer{width:var(--container-widget-width,var(--spacer-size));--align-self:var(--container-widget-align-self,initial);--flex-shrink:0}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container,.e-con>.elementor-widget-spacer>.elementor-widget-container{height:100%;width:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer{height:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner{height:var(--container-widget-height,var(--spacer-size))}.e-con-inner>.elementor-widget-spacer.elementor-widget-empty,.e-con>.elementor-widget-spacer.elementor-widget-empty{position:relative;min-height:22px;min-width:22px}.e-con-inner>.elementor-widget-spacer.elementor-widget-empty .elementor-widget-empty-icon,.e-con>.elementor-widget-spacer.elementor-widget-empty .elementor-widget-empty-icon{position:absolute;top:0;bottom:0;left:0;right:0;margin:auto;padding:0;width:22px;height:22px}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d785d22 elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"d785d22\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-10d330b\" data-id=\"10d330b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7671152 elementor-widget elementor-widget-spacer\" data-id=\"7671152\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-3781101 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3781101\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div data-dce-background-color=\"#FFFFFF\" class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-7486805\" data-id=\"7486805\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cace541 elementor-widget elementor-widget-menu-anchor\" data-id=\"cace541\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\nbody.elementor-page .elementor-widget-menu-anchor{margin-bottom:0}<\/style>\t\t<div class=\"elementor-menu-anchor\" id=\"about\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e5652e3 elementor-widget elementor-widget-heading\" data-id=\"e5652e3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">About Us<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-82432bd elementor-widget elementor-widget-text-editor\" data-id=\"82432bd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">While designing new processors to run ML models can lead to improved performance, making effective usage of processor features in a clean software toolchain can become a challenging endeavor. Celera has put together a team of compiler experts and software engineers who know how to exploit the best performance of your hardware and how to organize its toolchain to improve productivity. With Celera, your models run faster and more organized.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5227067 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5227067\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fce5a60\" data-id=\"fce5a60\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3ea1be9 elementor-widget elementor-widget-menu-anchor\" data-id=\"3ea1be9\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"technology\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14a7efa elementor-widget elementor-widget-heading\" data-id=\"14a7efa\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Our Technology<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-81c9477 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"81c9477\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div data-dce-background-color=\"#FFFFFF\" class=\"elementor-column elementor-col-33 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viewBox=\"0 0 44.82 29.502\"><g id=\"Grupo_41\" data-name=\"Grupo 41\" transform=\"translate(-159.829 -1681.558)\"><path id=\"Caminho_152\" data-name=\"Caminho 152\" 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transform=\"translate(5 -6)\" fill=\"#8db7e0\"><\/path><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Optimization<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9db7018 elementor-widget elementor-widget-text-editor\" data-id=\"9db7018\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Optimizing program performance is a complex task, particularly for ML models, which can have large memory footprints. From list scheduling algorithms to double-buffering methods to hide the latency of tensor transfers, our engineers know all techniques needed to optimize your code.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div data-dce-background-color=\"#FFFFFF\" class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-052946e\" data-id=\"052946e\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5fce6d6 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"5fce6d6\" data-element_type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<ul 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d=\"M615.755,1698.931l.743.044c.021.26.044.521.062.781s-.063.581.066.716.465.065.71.075c.2.008.4-.006.6,0,.178.009.232-.073.229-.239-.005-.384,0-.768,0-1.152,0-.143.05-.216.2-.214q.6,0,1.2,0c.152,0,.2.072.2.214,0,.384,0,.769,0,1.153,0,.166.053.242.23.239.384-.009.769-.009,1.153,0,.182,0,.227-.082.224-.243-.005-.368.006-.735,0-1.1-.005-.192.062-.268.258-.262.367.01.736.013,1.1,0,.212-.008.251.095.248.273-.007.367,0,.735,0,1.1,0,.172.062.241.234.234.2-.008.4-.007.6,0a1.607,1.607,0,0,1,1.571,1.572c.008.2.009.4,0,.6-.008.174.065.236.235.233.367-.007.735,0,1.1,0,.181,0,.279.04.272.25-.014.384-.007.768,0,1.152,0,.15-.066.206-.211.205-.384,0-.768,0-1.152,0-.163,0-.247.046-.243.226q.014.578,0,1.153c0,.179.076.231.24.228.385-.005.769,0,1.153,0,.143,0,.215.051.213.2q0,.6,0,1.2c0,.153-.074.2-.215.2-.384,0-.769.005-1.153,0-.167,0-.241.055-.238.231.008.384.009.769,0,1.153,0,.182.084.226.245.223.367-.005.735.007,1.1,0,.193-.005.268.064.262.259-.01.367-.013.735,0,1.1.008.214-.1.251-.274.248-.367-.006-.735,0-1.1,0-.173,0-.24.064-.233.235.008.2.007.4,0,.6a1.611,1.611,0,0,1-1.573,1.57c-.2.008-.4.009-.6,0-.175-.008-.235.067-.232.236.006.367,0,.735,0,1.1,0,.182-.042.278-.251.271-.367-.013-.735-.012-1.1,0-.2.006-.261-.075-.256-.265.009-.367,0-.735,0-1.1,0-.164-.047-.245-.227-.242q-.576.013-1.152,0c-.18,0-.23.077-.228.241.006.385,0,.769,0,1.153,0,.144-.053.214-.2.212q-.6,0-1.2,0c-.153,0-.2-.075-.2-.216,0-.384-.005-.768,0-1.153,0-.168-.056-.24-.231-.237-.384.008-.769.009-1.153,0-.183,0-.225.085-.223.245.005.367-.006.735,0,1.1.005.194-.066.267-.26.261-.367-.01-.735-.013-1.1,0-.215.008-.25-.1-.247-.274.006-.368,0-.736,0-1.1,0-.174-.066-.239-.236-.232-.2.008-.4.007-.6,0a1.6,1.6,0,0,1-1.569-1.571c-.008-.2-.009-.4,0-.6.007-.173-.063-.238-.234-.234-.351.007-.7.005-1.053,0-.306-.005-.312-.009-.317-.3-.006-.367,0-.735,0-1.1,0-.149.065-.207.21-.206.368,0,.736-.007,1.1,0,.2.006.3-.048.3-.277-.016-.367-.011-.735,0-1.1,0-.178-.073-.232-.239-.229-.384.005-.768,0-1.152,0-.143,0-.216-.049-.215-.2q.006-.6,0-1.2c0-.152.073-.2.215-.2.384,0,.768,0,1.152,0,.167,0,.243-.052.239-.229-.008-.384-.008-.769,0-1.153,0-.181-.082-.227-.243-.225-.384.005-.768,0-1.153,0-.145,0-.211-.057-.21-.206q.006-.6,0-1.2c0-.155.079-.2.218-.2.384,0,.769-.005,1.153,0,.171,0,.241-.062.234-.234-.008-.2-.007-.4,0-.6a1.608,1.608,0,0,1,1.571-1.571c.2-.008.4-.009.6,0,.173.007.238-.063.234-.234-.007-.351,0-.7,0-1.053.005-.306.009-.306.3-.318h.5Zm-1.6,8.011q0,2.23,0,4.462c0,.406.007.378.39.377q4.435,0,8.873,0c.356,0,.364-.006.364-.35q0-4.463,0-8.924c0-.344-.007-.351-.363-.351q-4.437,0-8.873,0c-.422,0-.391-.013-.391.426Q614.154,1704.761,614.154,1706.942Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_155\" data-name=\"Caminho 155\" d=\"M596.371,1707.77c-.7,0-1.4,0-2.1,0-.413,0-.416,0-.56.4l-2.717,7.475c-.022.063-.047.125-.07.187-.239.65-.4.765-1.111.765-2.2,0-4.411.008-6.616-.008a.582.582,0,0,0-.631.4,2.763,2.763,0,0,1-2.762,1.6,2.807,2.807,0,0,1-.787-5.405,2.757,2.757,0,0,1,3.522,1.352.676.676,0,0,0,.744.46c1.9-.018,3.809-.008,5.714-.008.526,0,.526,0,.7-.478q1.352-3.714,2.7-7.427c.04-.109.077-.22.119-.329a.857.857,0,0,1,.888-.594c.7.008,1.4,0,2.1,0,.969,0,1.938,0,2.907,0,.563,0,.492-.056.5.519,0,.267-.01.535,0,.8.011.217-.069.289-.286.286C597.875,1707.763,597.123,1707.77,596.371,1707.77Zm-15.116,8.011a1.2,1.2,0,1,0-1.2,1.213A1.214,1.214,0,0,0,581.255,1715.781Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_156\" data-name=\"Caminho 156\" d=\"M588,1709.374c-.568,0-1.136.011-1.7,0a.44.44,0,0,0-.481.313,2.7,2.7,0,0,1-2.558,1.691,2.8,2.8,0,0,1-.906-5.46,2.744,2.744,0,0,1,3.382,1.4.7.7,0,0,0,.793.466c1.051-.021,2.1-.007,3.156-.008.427,0,.4-.01.4.417,0,.3-.01.6,0,.9.009.212-.06.3-.282.29C589.2,1709.366,588.6,1709.374,588,1709.374Zm-4.753-2a1.2,1.2,0,1,0,1.217,1.2A1.213,1.213,0,0,0,583.248,1707.372Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_157\" data-name=\"Caminho 157\" d=\"M621.419,1703.746l.694.041c.021.243.056.486.06.73,0,.263-.021.527-.034.791-.261.018-.522.049-.784.051-.245,0-.581.063-.712-.068s-.069-.466-.067-.712c0-.261.033-.522.051-.783l.741-.038h.05Z\" fill=\"#8db7e0\"><\/path><\/g><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Compilers<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a428d3f elementor-widget elementor-widget-text-editor\" data-id=\"a428d3f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">We have designed LLVM-based compilers for various architectures, from regular x86 multicores to specialized Neuromorphic architectures. We know how to make the best usage of dedicated MAC units, sophisticated ISAs, and complex DMA engines.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div data-dce-background-color=\"#FFFFFF\" class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-5a22489\" data-id=\"5a22489\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6468b10 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"6468b10\" data-element_type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"35.492\" height=\"35.521\" viewBox=\"0 0 35.492 35.521\"><g id=\"Grupo_42\" data-name=\"Grupo 42\" transform=\"translate(-1003.28 -1689.99)\"><g id=\"Grupo_18\" data-name=\"Grupo 18\"><g id=\"Grupo_17\" data-name=\"Grupo 17\"><path id=\"Caminho_158\" data-name=\"Caminho 158\" d=\"M1009.56,1725.511c-.322-.074-.65-.131-.967-.224a6.9,6.9,0,0,1-5.172-5.54,7.043,7.043,0,0,1,5.36-8.247,6.458,6.458,0,0,1,2.648-.1,2.064,2.064,0,0,0,.207.025.936.936,0,0,0,.105,0l12.956-12.95c-.025-.565-.09-1.14-.071-1.713a7.063,7.063,0,0,1,8.668-6.589.642.642,0,0,1,.543.42.65.65,0,0,1-.215.687q-1.105,1.1-2.207,2.206l-1.4,1.393c.163.487.326.977.489,1.466s.32.963.486,1.461l2.982.992a2.581,2.581,0,0,1,.225-.3q1.579-1.586,3.164-3.163c.065-.066.131-.131.2-.195a.6.6,0,0,1,.629-.182.639.639,0,0,1,.435.533c.046.2.1.4.145.594v2.012a.581.581,0,0,0-.053.127,6.333,6.333,0,0,1-1.006,2.667,6.95,6.95,0,0,1-6.537,3.271c-.287-.015-.572-.065-.752-.086l-3.139,3.138a.838.838,0,0,0,.06.071c.384.384.773.763,1.151,1.153a.631.631,0,0,1,0,1,2.683,2.683,0,0,0-.3.338.625.625,0,0,0,.078.8.637.637,0,0,0,.791.109,2.129,2.129,0,0,0,.345-.287.634.634,0,0,1,1.045.009q3.484,3.478,6.962,6.963a4.7,4.7,0,0,1,1.29,2.332.514.514,0,0,0,.061.119v1.873c-.03.085-.064.168-.089.254a4.754,4.754,0,0,1-3.147,3.35c-.235.083-.479.142-.719.213h-1.526c-.029-.018-.055-.047-.085-.053a4.988,4.988,0,0,1-2.712-1.5q-3.335-3.356-6.689-6.692a2.866,2.866,0,0,1-.216-.226.585.585,0,0,1,.005-.817c.089-.106.2-.2.286-.3a.666.666,0,0,0,0-.937.675.675,0,0,0-.939.014c-.106.089-.2.2-.3.284a.571.571,0,0,1-.815-.012c-.175-.15-.331-.322-.5-.485l-.83-.823-3.128,3.127c.029.209.077.459.1.712a7.1,7.1,0,0,1-5.165,7.445c-.375.108-.761.175-1.142.261Zm22.46-34.231-.057-.128c-.342.024-.687.027-1.026.075a5.732,5.732,0,0,0-4.411,3,5.574,5.574,0,0,0-.595,4.055.935.935,0,0,1-.268.967q-6.571,6.55-13.124,13.121a.921.921,0,0,1-.9.279,6.774,6.774,0,0,0-1.3-.15,5.946,5.946,0,0,0-5.282,8.669,5.964,5.964,0,0,0,10.725-.269,5.531,5.531,0,0,0,.391-3.689.911.911,0,0,1,.257-.94q6.583-6.564,13.15-13.145a.848.848,0,0,1,.87-.266,5.636,5.636,0,0,0,2.612.006,5.877,5.877,0,0,0,4.52-4.957,3.674,3.674,0,0,0,.028-1.123c-.137.133-.226.218-.313.3-.891.891-1.787,1.778-2.671,2.676a.759.759,0,0,1-.875.211q-1.6-.554-3.219-1.073a.9.9,0,0,1-.628-.628c-.345-1.065-.694-2.129-1.06-3.186a.8.8,0,0,1,.229-.934c.9-.88,1.785-1.78,2.678-2.669A2.367,2.367,0,0,1,1032.02,1691.28Zm-5.614,16.783-5.026,5.049.879.872a1.919,1.919,0,0,1,2.433.123,1.77,1.77,0,0,1,.535.951,1.844,1.844,0,0,1-.332,1.513l6.812,6.812,4.991-5-6.819-6.79a1.85,1.85,0,0,1-2.361-.089,1.817,1.817,0,0,1-.584-1.037,1.9,1.9,0,0,1,.369-1.506Zm11.011,11.549-4.522,4.515a3.594,3.594,0,0,0,3.716-.871A3.547,3.547,0,0,0,1037.417,1719.612Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_159\" data-name=\"Caminho 159\" d=\"M1009.876,1693.338l-3.5-2-1.737,1.726c.016.035.039.1.071.155.6,1.044,1.2,2.084,1.784,3.132a.556.556,0,0,0,.393.3c.562.131,1.119.282,1.681.415a1.211,1.211,0,0,1,.592.334q3.676,3.684,7.358,7.361c.063.064.122.132.175.19l-.838.808c-.058-.055-.157-.144-.25-.238q-3.521-3.519-7.038-7.043a1.15,1.15,0,0,0-.584-.338c-.586-.132-1.164-.3-1.748-.433a.858.858,0,0,1-.583-.445c-.736-1.3-1.479-2.587-2.216-3.883a.653.653,0,0,1,.137-.957q1.062-1.071,2.133-2.135a.655.655,0,0,1,.983-.129c1.275.726,2.545,1.46,3.823,2.18a.987.987,0,0,1,.512.7c.125.564.278,1.121.413,1.682a1.075,1.075,0,0,0,.3.529q3.538,3.526,7.069,7.061a3.44,3.44,0,0,0,.3.235l-.887.837c-.036-.034-.12-.108-.2-.188q-3.632-3.629-7.266-7.256a1.489,1.489,0,0,1-.415-.74C1010.191,1694.583,1010.036,1693.979,1009.876,1693.338Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_160\" data-name=\"Caminho 160\" d=\"M1010.4,1721.971c-.555,0-1.11-.007-1.665,0a.716.716,0,0,1-.685-.38q-.832-1.4-1.676-2.793a.687.687,0,0,1,.006-.779q.844-1.393,1.676-2.793a.675.675,0,0,1,.624-.363q1.716,0,3.434,0a.651.651,0,0,1,.6.345q.843,1.413,1.7,2.821a.7.7,0,0,1-.008.781q-.844,1.393-1.678,2.792a.7.7,0,0,1-.656.368C1011.508,1721.965,1010.953,1721.971,1010.4,1721.971Zm-1.462-5.906-1.411,2.351,1.417,2.361h2.893l1.415-2.361-1.41-2.351Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_161\" data-name=\"Caminho 161\" d=\"M1014.9,1714.611l-.72-.707,13.008-13,.679.741Z\" fill=\"#8db7e0\"><\/path><path id=\"Caminho_162\" data-name=\"Caminho 162\" d=\"M1027.993,1713.911l5.842,5.84-.763.739-5.847-5.847Z\" fill=\"#8db7e0\"><\/path><\/g><\/g><\/g><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Toolchains<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e2d576 elementor-widget elementor-widget-text-editor\" data-id=\"8e2d576\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">One of the biggest challenges in designing ML models is empowering developers with a seamless toolchain integrated with industry standards like Tensorflow, JAX\/XLA, Pytorch, Glow, and ONNX. Our team has already integrated solutions to all these toolchains.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8bc4802 elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"8bc4802\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8422a1e\" data-id=\"8422a1e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-84f07c2 elementor-widget elementor-widget-menu-anchor\" data-id=\"84f07c2\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-menu-anchor\" id=\"cases\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71ac06f elementor-widget elementor-widget-heading\" data-id=\"71ac06f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Some of our cases<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-84bb76d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"84bb76d\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-a233ed2\" data-id=\"a233ed2\" data-element_type=\"column\" id=\"01\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8aa4a51 dce_masking-none elementor-widget elementor-widget-image\" data-id=\"8aa4a51\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.23.0 - 05-08-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"170\" height=\"75\" src=\"https:\/\/celera.ai\/wp-content\/uploads\/2022\/07\/Grupo-43.png\" class=\"attachment-large size-large wp-image-89\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-415d5a4 elementor-widget elementor-widget-heading\" data-id=\"415d5a4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">LG XLA Optimizer <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fec378a elementor-widget elementor-widget-text-editor\" data-id=\"fec378a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">LG Electronics Neuromorphic processor is a 32-core architecture where each core is composed of a RISCV extended with new instructions dedicated to ML operations. Celera used TensorFlow\/XLA and our convolution slicing optimization algorithm to improve LG\u2019s model performance.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"dce-visibility-element-hidden dce-visibility-original-content elementor-element elementor-element-c9299df off-card1 elementor-author-box--image-valign-middle elementor-widget elementor-widget-author-box\" data-id=\"c9299df\" data-element_type=\"widget\" data-settings=\"{&quot;enabled_visibility&quot;:&quot;yes&quot;}\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<link rel=\"stylesheet\" href=\"https:\/\/celera.ai\/wp-content\/plugins\/elementor-pro\/assets\/css\/widget-theme-elements.min.css\">\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<div  class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/celera.ai\/wp-content\/uploads\/2022\/07\/Michael.jpeg\">\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-author-box__bio\">\n\t\t\t\t\t\t<p>Working with such a competent and high-quality team has been a great pleasure, and I would like to thank everyone involved for the successful cooperation.<br \/>\n(Michael Frank, Roadmap Architect, LG Silicon Valley Lab, 2019)<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<script>\n\t\t\t\t\t\tjQuery(function () {\n\t\t\t\t\t\t\tjQuery('#01').on('mouseover', function () {\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjQuery('.off-card2,.off-card3').stop();\n\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ Dynamic Content for Elementor\n\t\t\t\t\t\t\t\t\t\t\/\/ Hide other elements\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ The element has got a CSS Class and it doesn't have a CSS ID\n\t\t\t\t\t\t\t\t\t\t\tjQuery('.off-card2,.off-card3').not('.off-card1').addClass(\"dce-visibility-element-hidden\").hide();\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ Dynamic Content for Elementor\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ The element has got a CSS Class and it doesn't have a CSS ID\n\t\t\t\t\t\t\t\t\t\tjQuery('.off-card1').removeClass(\"dce-visibility-element-hidden\").show();\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tif (jQuery(this).attr('href') == '#') {\n\t\t\t\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\t});\n\t\t\t\t});\n\t\t\t\t<\/script>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-1cc3b2c\" data-id=\"1cc3b2c\" data-element_type=\"column\" id=\"02\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6e254de dce_masking-none elementor-widget elementor-widget-image\" data-id=\"6e254de\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"300\" height=\"30\" src=\"https:\/\/celera.ai\/wp-content\/uploads\/2022\/07\/Retangulo-38.png\" class=\"attachment-large size-large wp-image-91\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2c48a6 elementor-widget elementor-widget-heading\" data-id=\"c2c48a6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">SilicoNeuro Glow Compiler<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf4a6f2 elementor-widget elementor-widget-text-editor\" data-id=\"cf4a6f2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Celera has ported the Glow Compiler toolchain to the SilicoNeuro NMP processor. Quantization and convolution slicing optimization techniques were employed in the design to create a smooth path from ONNX to Glow and LLVM code generation.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"dce-visibility-element-hidden dce-visibility-original-content elementor-element elementor-element-d2f15ee off-card2 elementor-author-box--image-valign-middle elementor-widget elementor-widget-author-box\" data-id=\"d2f15ee\" data-element_type=\"widget\" id=\"card2\" data-settings=\"{&quot;enabled_visibility&quot;:&quot;yes&quot;}\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<div  class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/celera.ai\/wp-content\/uploads\/2022\/07\/Changsoo.jpeg\">\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-author-box__bio\">\n\t\t\t\t\t\t<p>Celera's team delivered an optimized Glow compiler for the NMP processor with professionalism and within the project schedule.<br \/>\n(Chang Soo Kim, CEO, AiM Future, 2022) <\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<script>\n\t\t\t\t\t\tjQuery(function () {\n\t\t\t\t\t\t\tjQuery('#02').on('mouseover', function () {\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjQuery('.off-card1,.off-card3').stop();\n\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ Dynamic Content for Elementor\n\t\t\t\t\t\t\t\t\t\t\/\/ Hide other elements\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ The element has got a CSS ID and a CSS Class\n\t\t\t\t\t\t\t\t\t\t\tjQuery('.off-card1,.off-card3').not('#card2.off-card2').addClass(\"dce-visibility-element-hidden\").hide();\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ Dynamic Content for Elementor\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\/\/ The element has got a CSS ID and a CSS Class\n\t\t\t\t\t\t\t\t\t\tjQuery(\"#card2.off-card2\").removeClass(\"dce-visibility-element-hidden\").show();\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tif (jQuery(this).attr('href') == '#') {\n\t\t\t\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\t});\n\t\t\t\t});\n\t\t\t\t<\/script>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-d804db5\" data-id=\"d804db5\" data-element_type=\"column\" id=\"03\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3dada2b dce_masking-none elementor-widget elementor-widget-image\" data-id=\"3dada2b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"216\" height=\"45\" src=\"https:\/\/celera.ai\/wp-content\/uploads\/2022\/07\/7mpboc.tif.png\" class=\"attachment-large size-large wp-image-95\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71b1d39 elementor-widget elementor-widget-heading\" data-id=\"71b1d39\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">ETRI NEST Toolchain<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2b6f5bb elementor-widget elementor-widget-text-editor\" data-id=\"2b6f5bb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">ETRI has developed an in-house toolchain that uses partitioning and parallelism algorithms to improve the performance of ML models. Celera has integrated its convolution slicing and optimizing compiling technology into NEST, enabling improved performance in one of NEST\u2019s neuromorphic acceleration engines.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-275e08c off-card3 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-author-box\" data-id=\"275e08c\" data-element_type=\"widget\" id=\"card3\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<div  class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/celera.ai\/wp-content\/uploads\/2022\/07\/Grupo-39.png\">\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-author-box__bio\">\n\t\t\t\t\t\t<p>The team working at Celera has delivered a quality work for the NEST toolchain. (Taeho Kim, Assistant VP, ETRI, 2021)<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>About Us While designing new processors to run ML models can lead to improved performance, making effective usage of processor features in a clean software toolchain can become a challenging endeavor. Celera has put together a team of compiler experts and software engineers who know how to exploit the best performance of your hardware and [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-7","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/celera.ai\/index.php?rest_route=\/wp\/v2\/pages\/7","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/celera.ai\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/celera.ai\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/celera.ai\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/celera.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7"}],"version-history":[{"count":634,"href":"https:\/\/celera.ai\/index.php?rest_route=\/wp\/v2\/pages\/7\/revisions"}],"predecessor-version":[{"id":773,"href":"https:\/\/celera.ai\/index.php?rest_route=\/wp\/v2\/pages\/7\/revisions\/773"}],"wp:attachment":[{"href":"https:\/\/celera.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}