{"id":14971,"date":"2026-02-13T08:00:00","date_gmt":"2026-02-13T13:00:00","guid":{"rendered":"https:\/\/gf.com\/?p=14971"},"modified":"2026-02-11T15:56:15","modified_gmt":"2026-02-11T20:56:15","slug":"powering-the-physical-ai-era-how-globalfoundries-enables-real-time-machines-that-sense-think-act-and-communicate","status":"publish","type":"post","link":"https:\/\/gf.com\/blog\/powering-the-physical-ai-era-how-globalfoundries-enables-real-time-machines-that-sense-think-act-and-communicate\/","title":{"rendered":"Powering the Physical AI Era: How GlobalFoundries Enables Real-Time Machines That Sense, Think, Act, and Communicate\u00a0"},"content":{"rendered":"\n<p><strong>Ed Kaste, SVP of Ultra-Low Power CMOS Business, GlobalFoundries<\/strong>\u00a0<\/p>\n\n\n\n<p>Today, Physical AI is already taking shape in the real world\u2014appearing in everything from self\u2011driving vehicles navigating cities from San Francisco to Shenzhen,&nbsp;to&nbsp;autonomous robots&nbsp;operating&nbsp;in industrial warehouses and drones delivering packages.&nbsp;But the future of Physical AI will extend even further, spanning everything from humanoid robots to autonomous imaging systems in healthcare and a wide range of other real\u2011world applications.&nbsp;This next phase of AI is bringing AI beyond data centers and directly into the physical world in the form of machines that interact with their environments in real time.&nbsp;<\/p>\n\n\n\n<p>However, delivering these capabilities at scale introduces a new set of constraints and opportunities for semiconductor technology.&nbsp;Multi-modal sensing, distributed intelligence,&nbsp;actuation,&nbsp;and power efficiency&nbsp;become as critical as performance itself. Purpose-built semiconductor platforms are the foundation that will enable Physical AI to move from early adoption to widespread deployment.&nbsp;<\/p>\n\n\n\n<p><strong>Purpose-built semiconductor platforms for Physical AI&nbsp;<\/strong>&nbsp;<\/p>\n\n\n\n<p>Physical AI is introducing broader workloads that are reshaping the requirements for semiconductors. The requirements of Physical AI are creating a massive opportunity for GF to deliver reliable, energy-efficient, highly integrated platforms that can adapt over time.&nbsp;<\/p>\n\n\n\n<p>Here\u2019s&nbsp;how our platforms are enabling this next wave of Physical AI:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GF\u2019s industry-leading<\/strong>\u00a0<strong>FDX platform<\/strong>\u00a0is ideally suited for applications\u00a0in\u00a0Physical AI that are\u00a0optimized\u00a0for\u00a0long battery life in small form factors, thanks to its\u00a0ultra-low power and\u00a0low\u00a0leakage\u00a0capabilities,\u00a0superior RF performance,\u00a0integrated\u00a0power\u00a0management\u00a0and\u00a0highly reliable\u00a0operation up to 150 degrees Celsius.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GF&#8217;s differentiated\u00a0FinFET\u00a0platform<\/strong>\u00a0provides increased\u00a0performance at the right power profile, fully\u00a0optimized\u00a0for integrated solutions, enabling\u00a0efficient\u00a0sensing,\u00a0real-time processing,\u00a0and\u00a0seamless\u00a0communication in real-world environments.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Memory solutions including MRAM and RRAM\u00a0<\/strong>offer\u00a0embedded non-volatile memory options with\u00a0low power consumption\u00a0and\u00a0the\u00a0fastest access times in the market,\u00a0allowing customers to build differentiated systems from scratch with\u00a0pre-validated\u00a0memory IP. This is critical to future-proof Physical AI designs as traditional memory scaling faces both physical and economic limits.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Silicon photonics and RF innovation<\/strong>\u00a0are driving high-speed connectivity by increasing speed and bandwidth of interconnects within and outside of the application, to communicate reliably across billions of devices at the lowest possible power.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Advanced packaging and heterogeneous integration<\/strong>\u00a0further enable Physical AI by bringing together diverse technologies\u2014compute, memory,\u00a0RF\u00a0and power\u2014into compact, efficient systems\u00a0optimized\u00a0for distributed deployment.\u00a0<\/li>\n<\/ul>\n\n\n\n<p><strong>The real-time operating model behind Physical AI<\/strong>&nbsp;<\/p>\n\n\n\n<p>As AI undergoes this fundamental shift to be present in the real world,&nbsp;applications in&nbsp;Physical AI must respond in real time&nbsp;to the environment around it.&nbsp;<a href=\"https:\/\/gf.com\/blog\/physical-ai-moves-center-stage-why-data-center-investments-are-enabling-real-world-robotics\/\" target=\"_blank\" rel=\"noreferrer noopener\">In our last blog<\/a>,&nbsp;our Chief Business Officer,&nbsp;Mike Hogan,&nbsp;introduced a simple but powerful framework that defines how Physical AI functions:&nbsp;<em>Sense \u2013 Think \u2013 Act \u2013 Communicate.<\/em>&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Sense:<\/strong>\u00a0Capture data from the physical environment using multimodal sensors such as\u00a0audio, haptics, optical,\u00a0radar, and environmental sensors.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Think:<\/strong>\u00a0Process and interpret that data locally to make real-time decisions,\u00a0in\u00a0deterministic, safe, and secure way.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Act:<\/strong>\u00a0Execute precise,\u00a0timely\u00a0actions through motors\u00a0or actuators with precision\u00a0feedback loops.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Communicate:<\/strong>\u00a0Exchange data reliably and securely across distributed systems, from edge to cloud and across devices.\u00a0<\/li>\n<\/ul>\n\n\n\n<p>However, any weakness\u2014&nbsp;whether&nbsp;in latency, power efficiency,&nbsp;security&nbsp;or&nbsp;reliability\u2014can degrade overall system performance.&nbsp;That\u2019s&nbsp;why looking ahead, Physical AI systems will become more&nbsp;customized&nbsp;and adaptive, to&nbsp;optimize&nbsp;not&nbsp;just&nbsp;for&nbsp;compute&nbsp;but for real-world operations over long lifecycles.&nbsp;<\/p>\n\n\n\n<p><strong>Overcoming power and latency constraints of Physical AI<\/strong>&nbsp;<\/p>\n\n\n\n<p>Power&nbsp;and latency are fundamental&nbsp;system-level&nbsp;constraints&nbsp;that&nbsp;shape&nbsp;what is possible&nbsp;in&nbsp;Physical AI. These&nbsp;applications&nbsp;operate&nbsp;continuously in confined thermal environments,&nbsp;often&nbsp;times&nbsp;without direct access to abundant energy, while simultaneously requiring real-time responsiveness. As semiconductor content increases, inefficient power consumption&nbsp;and excessive latency&nbsp;can limit performance, reduce&nbsp;reliability&nbsp;and shorten operational life.&nbsp;<\/p>\n\n\n\n<p>Optimizing for&nbsp;power efficiency&nbsp;and ultra-low latency&nbsp;enables Physical AI systems to do more with less&nbsp;under power,&nbsp;thermal&nbsp;and computing constraints. This makes&nbsp;innovating&nbsp;semiconductor platforms essential to scaling Physical AI beyond&nbsp;pilots, and eventually&nbsp;into mission-critical environments.&nbsp;<\/p>\n\n\n\n<p><strong>Enabling software-defined, distributed intelligence<\/strong>&nbsp;<\/p>\n\n\n\n<p>As Physical AI systems evolve, architectures are&nbsp;shifting away&nbsp;from centralized compute toward distributed intelligence.&nbsp;Rather than&nbsp;sending&nbsp;all data to&nbsp;the cloud or a&nbsp;single processor,&nbsp;intelligence is&nbsp;being&nbsp;placed&nbsp;at the interface with the real world,&nbsp;so that they are&nbsp;closer to where data is&nbsp;generated&nbsp;and actions are taken.&nbsp;<\/p>\n\n\n\n<p>Software-defined&nbsp;architectures play&nbsp;a key role in this transition. By decoupling hardware from software, developers can continuously upgrade features&nbsp;and have the flexibility to support&nbsp;evolving AI models without&nbsp;having to&nbsp;redesign&nbsp;the actual&nbsp;hardware. This is especially critical in long-lived systems such as vehicles, industrial&nbsp;equipment&nbsp;and robotics platforms.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"458\" src=\"https:\/\/gf.com\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-11-134004-1024x458.png\" alt=\"\" class=\"wp-image-14972\" style=\"width:683px;height:auto\" srcset=\"https:\/\/gf.com\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-11-134004-1024x458.png 1024w, https:\/\/gf.com\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-11-134004-300x134.png 300w, https:\/\/gf.com\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-11-134004-768x344.png 768w, https:\/\/gf.com\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-11-134004.png 1262w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p><strong>Physical AI&nbsp;today:&nbsp;Software&nbsp;defined&nbsp;vehicles<\/strong>&nbsp;<\/p>\n\n\n\n<p>One of the most visible examples of Physical AI today is the software-defined vehicle&nbsp;(SDV).&nbsp;Today\u2019s modern&nbsp;vehicles integrate hundreds of chips to support advanced driver&nbsp;assistance&nbsp;systems (ADAS), infotainment,&nbsp;connectivity&nbsp;and battery management.&nbsp;However, as&nbsp;autonomy,&nbsp;electrification&nbsp;and connectivity accelerate, semiconductor content per vehicle continues to rise.&nbsp;In just the last five years alone, the average semiconductor content per vehicle has risen from $700 to $1,000 and&nbsp;<a href=\"https:\/\/www.spglobal.com\/automotive-insights\/en\/blogs\/2025\/02\/semiconductor-tariffs-impact-automotive-costs#:~:text=The%20value%20of%20electronic%20control,greater%20use%20of%20advanced%20electronics.\" target=\"_blank\" rel=\"noreferrer noopener\">S&amp;P Global Mobility<\/a>&nbsp;estimates this number&nbsp;to continue growing to&nbsp;approximately $1,400&nbsp;through the end of the decade.&nbsp;<\/p>\n\n\n\n<p>These systems rely on high-performance sensors, real-time&nbsp;processing&nbsp;and precise actuation&nbsp;to improve automotive safety and user experience\u2014all while&nbsp;operating&nbsp;under strict power and thermal constraints.&nbsp;<\/p>\n\n\n\n<p><strong>Physical AI&nbsp;tomorrow:&nbsp;Humanoid&nbsp;robots<\/strong>&nbsp;<\/p>\n\n\n\n<p>The same principles extend&nbsp;into&nbsp;emerging humanoid systems,&nbsp;which&nbsp;need&nbsp;even higher degrees of flexibility to support evolving AI models, sensor fusion&nbsp;algorithms&nbsp;and autonomy stacks.&nbsp;That\u2019s&nbsp;because humanoid&nbsp;robots&nbsp;require multimodal sensing to perceive their environments, distributed intelligence to process data with ultra-low latency and precise motor control to execute fluid, human-like motion&nbsp;in real-time&nbsp;with dozens of degrees of freedom.&nbsp;&nbsp;<\/p>\n\n\n\n<p>It\u2019s&nbsp;no surprise that a high-end industrial humanoid has&nbsp;semiconductor content&nbsp;that exceeds&nbsp;SDVs&nbsp;by&nbsp;up to four times.&nbsp;These growing silicon footprints&nbsp;make one thing clear: Scaling Physical AI will depend on platforms that can deliver real-time performance within tight power, thermal and reliability limits.&nbsp;<\/p>\n\n\n\n<p><strong>Building the foundation for the&nbsp;Physical&nbsp;AI&nbsp;future<\/strong>&nbsp;<\/p>\n\n\n\n<p>As the Physical AI wave pushes intelligence from the cloud&nbsp;into the physical world, success is no longer&nbsp;defined by raw compute alone, but by the ability to deliver reliable, energy-efficient, and adaptable systems at scale.&nbsp;At GF,&nbsp;we\u2019re&nbsp;continuously looking for&nbsp;opportunities to enhance our technology platform for&nbsp;this&nbsp;future designed for sensing, real-time decision-making,&nbsp;actuation&nbsp;and communication.&nbsp;<\/p>\n\n\n\n<p>Following&nbsp;our&nbsp;<a href=\"https:\/\/gf.com\/gf-press-release\/globalfoundries-completes-acquisition-of-mips\/\" target=\"_blank\" rel=\"noreferrer noopener\">recent acquisition of MIPS<\/a>,&nbsp;we\u2019ve&nbsp;layered&nbsp;our platforms with MIPS\u2019 suite to better target the growing Physical AI opportunity. In the next installment of this blog,&nbsp;we\u2019ll&nbsp;chat with MIPS CEO,&nbsp;Sameer Wasson,&nbsp;on&nbsp;how&nbsp;we\u2019ve&nbsp;combined MIPS\u2019 architecture, IP &amp; design with GF\u2019s optimized process technologies to advance&nbsp;compute workloads&nbsp;and&nbsp;deliver the deterministic real-time performance that Physical AI&nbsp;requires.&nbsp;<\/p>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"601\" src=\"https:\/\/gf.com\/wp-content\/uploads\/2022\/09\/Ed-Kaste.jpg\" alt=\"\" class=\"wp-image-7945\" srcset=\"https:\/\/gf.com\/wp-content\/uploads\/2022\/09\/Ed-Kaste.jpg 900w, https:\/\/gf.com\/wp-content\/uploads\/2022\/09\/Ed-Kaste-300x200.jpg 300w, https:\/\/gf.com\/wp-content\/uploads\/2022\/09\/Ed-Kaste-768x513.jpg 768w, https:\/\/gf.com\/wp-content\/uploads\/2022\/09\/Ed-Kaste-600x400.jpg 600w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><figcaption class=\"wp-element-caption\">Right click to save the high resolution image<\/figcaption><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<p><em>Ed Kaste is Senior Vice President of Ultra\u2011low Power Business at GF, where he leads the company\u2019s ultra\u2011low power platform strategy enabling differentiated solutions across smart mobile, IoT, automotive, communications infrastructure, data center, and aerospace and defense markets. Previously, he held senior leadership roles spanning product management, IoT, and the FDX\u2122 business, with a focus on driving growth through application\u2011driven semiconductor innovation. He joined GlobalFoundries in 2015 following leadership roles in semiconductor research, development, and manufacturing at IBM.<\/em>\u00a0<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Ed Kaste, SVP of Ultra-Low Power CMOS Business, GlobalFoundries\u00a0 Today, Physical AI is already taking shape in the real world\u2014appearing [&hellip;]<\/p>\n","protected":false},"author":37,"featured_media":14973,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"market_tags":[],"article_type":[2],"class_list":["post-14971","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","article_type-blog",""],"acf":[],"mb":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Powering the Physical AI Era: How 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