Embedded AI enables dedicated functions within larger systems. These AI chips power countless devices—robotic arms, smart thermostats, security cameras, medical instruments, drones, and vehicles—enhancing functionality and decision-making at the edge.
ChatGPT is one of the most visited websites in the world. Along with Gemini, Perplexity AI, Grok, and many others, online AI tools are increasingly popular and specialized. This is leading to more power-hungry AI data centers, where hundreds of thousands of GPU chips run at upwards of 1,000 watts each. [1]
But millions of lower power AI chips are running quietly in edge applications all around us.
In smart homes, embedded AI powers thermostats, voice/image recognition, and security. In factories, it drives automated quality control, predictive maintenance, and robotic assembly.
Figure 1 – Embedded AI Systems in Industry Provide Fast, Local Processing to Enhance Production and Safety. [2]
Using local AI inference, these systems make independent decisions, predict outcomes, and automate operations in real time. Connected via the Internet of Things (IoT), they share data and improve interoperability, making homes and factories smarter and more efficient.
AI Technologies in Embedded Systems
- AI vs. ML: Artificial Intelligence (AI) includes deep learning that uses artificial neural networks to process unstructured data. Machine learning (ML), a subset of AI, focuses on training algorithms to learn from data and adapt over time.
- Discriminative AI: Embedded systems typically use discriminative AI—optimized for data analysis and evaluation—requiring lower compute power than generative models.
Embedded AI Chips and Cooling Needs
AI processors and modules in embedded applications are not the high-powered versions in data centers. For those, liquid cooling with constant monitoring is essential.
Figure 2 – Intel FPGAs Support Real-Time Deep Learning Inference for Embedded Systems and Data Centers. [4, 5]
Embedded AI processors often come in compact system-on-module (SOM) formats that include CPUs, memory, and specialized chips like GPUs or DSPs. These modules prioritize space efficiency and typically rely on air cooling—either passive or fan-assisted—rather than the liquid cooling found in high-wattage data centers.
Following are some popular AI processors and approved heat sinks.
AMD Kria™ SOMs
The AMD Kria K24 SOM runs on as little as 2.5 watts and typically uses a passive (fan-less) heat sink. Its low power and compact size allow it to be installed close to the processes it manages, such as intelligent motor control. The more capable Kria K26 SOM supports higher-end tasks like machine vision and robotic planning and may require active cooling. [6]
Figure 3 –The AMD Kria K24 and K26 SOMs Can Be Used for Sophisticated Robotic Applications. The K24 Provides Intelligent Motor Control. The K26 Manages Complex Machine Vision. [6]
In the above robotics application, different heat sinks are available to cool the K24 and K26 SOMs. These come in varieties for providing optimum levels of air cooling, as well as for fitting available spaces. The K24 SOM can be cooled with a passive (fan-less) sink. Depending on its application, the K26 SOM may need an active heat sink. Examples of heat sinks for cooling the K26 SOM are below. [7]
Figure 4 – Fan-assisted Heat Sinks, Like the Above ATS Model May be Needed for Cooling AMD Kria K26 System-on-Modules. In Some Applications, Passive (fan-less) Heat Sinks are Sufficient.
Figure 5 – Three Passive Heat Sinks Developed to Cool AMD Kria K24 SOMs. The Taller Finned Versions Provide More Cooling Performance but Need More Headroom and are Heavier. [8]
NVIDIA Jetson Modules
Widely used NVIDIA Jetson modules power a wide range of AI in embedded systems. These compact, powerful modules enable AI solutions in manufacturing, logistics, and healthcare. They leverage NVIDIA’s GPU technology for accelerated AI computations.
In the Jetson module family, Orin systems are specifically engineered to provide high-speed support for a wide range of sensors, enabling seamless integration with various edge AI applications.
One of these, the Jetson AGX Orin series, uses just 15 to 75 watts of power depending on the specific module, workload, and external factors such as local temperatures. They’re designed for passive cooling to manage heat in applications with prolonged operating temperatures, where fans could be affected by dust and debris. [9]
Figure 6 – Top: NVIDIA’s Jetson AGX Orin Module Features an AI Accelerator Graphic Chip and an Ampere GPU Architecture Chip in One Package. It Can be Passively Cooled with a Specially-Designed, NVIDIA-Approved ATS Heat Sink. [9,10]
Bottom: The Many Uses of Orin Modules Include Embedding in Zipline Delivery Drones [11]
The Orin, another Jetson module, is a small, powerful computer for embedded AI applications connected to the IoT. Its capabilities include deep learning, computer vision, graphics, and multimedia.
Figure 7 – Top: An NVIDIA Jetson Orin Nano Module and a Specially-Designed ATS Active Heat Sink. [12, 10] Bottom: Multiple Security Cameras and Sensors Feed Visual Data to an Orin Nano Module Whose AI Detects Unusual Activities. [13]
One application for Orin Nano modules is in security surveillance systems. Cameras and sensors are placed in strategic locations. The Orin Nano module processes their visual data, detecting unusual activities and triggering alerts when identified by the AI.
When Air Cooling Isn’t Enough
One exception to air cooling for embedded processors is in some smart phones. Tasked to perform ever more functions, including AI, their increasingly powerful chips require higher performance cooling.
For example, Qualcomm Snapdragon 8-series chips, used in phones like the OnePlus 13, generate significant heat under heavy loads. Vapor chambers help dissipate that heat across a broader surface for effective cooling without active fans.
Figure 8 – Top: The Top-Rated OnePlus 13 Phone Features a Qualcomm Snapdragon 8 Elite Chip. Botton: A Teardown Video Reveals the Vapor Chamber for Cooling the Snapdragon Chip. [14,15]
Embedded AI Efficiency
Embedded AI continues to gain ground due to its compact design, low latency, and localized processing. Its benefits include:
- Reduced network load by transmitting processed insights rather than raw data
- Lower system cost vs. cloud-based AI
- Lower power consumption, enabling simpler and cheaper cooling solutions
With AI now embedded across sectors—from smart homes to drones to industrial robotics—thermal management solutions are evolving alongside to ensure performance and longevity.
References
- MIT Technology Review, https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/
- GIGAIPC, https://www.gigaipc.com/en/solution-detail/Machine-Vision/
- Embedded, https://www.embedded.com/ai-efficiency-will-depend-on-model-size/
- Intel, https://www.intel.com/content/www/us/en/software/programmable/fpga-ai-suite/overview.html
- Mirabilis Design, https://www.mirabilisdesign.com/intel-fpga-neural-processor-ai/
- Electronic Design, https://www.electronicdesign.com/technologies/industrial/boards/video/21273991/a-look-inside-amds-kria-k24-system-on-module
- AMD, https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/
- Advanced Thermal Solutions, Inc., https://www.qats.com/Heat-Sinks/Device-Specific-AMD-Kria-K26
- NVIDIA, https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/
- Advanced Thermal Solutions, Inc., https://www.qats.com/Heat-Sinks/Device-Specific-NVIDIA
- Things Embedded, https://things-embedded.com/us/nvidia-jetson/orin/agx/
- NVIDIA, https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/product-development/
- Prox PC, https://www.proxpc.com/blogs/case-studies-real-world-applications-of-nvidia-jetson-orin-nano
- Tom’s Guide, https://www.tomsguide.com/phones/oneplus-phones/oneplus-13-is-official-and-one-of-the-first-snapdragon-8-elite-powered-phones
- PBKreviews, https://www.youtube.com/watch?v=WqJq3-ngL2Q
