Skip to main content


AI Workloads Scale with Next-Gen Processors and COM Express

embedded computing

X-rays, ultrasounds, and endoscopy machines generate massive volumes of data—sometimes too much to make sense of. In response, medical device OEMs integrate AI directly into medical imaging and diagnostics machines to make screening procedures more efficient, effective, and accessible for clinicians and patients alike.

Supporting AI-enabled medical imaging and diagnostics requires high-end hardware with the graphics and compute performance to execute intelligent imaging workloads in real time. Until recently, the easiest way to enable these capabilities was through discrete accelerators—an approach that can be expensive and inefficient in terms of upfront hardware costs and power consumption.

But by far the most costly design decision is the wrong system architecture. AI is evolving rapidly, so without flexible, adaptable, and upgradable system hardware, equipment can become obsolete before it is adequately broken in.

“AI workloads are advancing so quickly, it’s sort of dangerous when you start talking about hardware at all,” says Zeljko Loncaric, Market Segment Manager for Infrastructure at congatec AG, a global leader in embedded solutions. “That’s one of the most significant challenges facing medical device designers. They also face hurdles in implementing newer functionality in long-lifecycle systems.”

COM Express modules based on Intel® Core Ultra mobile processors address these challenges. They offer superior performance and efficiency in AI workload processing thanks to integrated GPUs and NPUs. And their inherent modularity streamlines the initial design process while enabling easy upgrades, processor generation over processor generation.

#AI #technology represents a meaningful advancement for #medical imaging, with the potential to significantly improve diagnostic efficiency and accuracy. @congatecAG via @insightdottech

Balancing Edge AI Longevity and Innovation in Embedded Computing

Because medical imaging devices must undergo a comprehensive certification process before they can be used, their lifecycles tend to average a decade or more. Meanwhile, AI technology represents a meaningful advancement for medical imaging, with the potential to significantly improve diagnostic efficiency and accuracy in ultrasounds, mobile ultrasounds, endoscopy machines, X-rays, and more.

But faced with the time and expense of redesigning and recertifying a medical device, OEMs hesitate to transition to next-generation platforms that support AI without an extremely compelling business case. And without being able to answer how long a system design will remain relevant, that business case becomes less compelling.

Enter new Intel Core Ultra Mobile processors, the first x86 processors to integrate an NPU, and one of the most power-efficient SoC families on the market today. The integrated NPU enables support for advanced AI workloads without the added cost and complexity of a discrete accelerator. When paired with the SoC’s leading performance-per-watt, medical device designers can better manage power consumption and thermal efficiency in resource-constrained edge AI deployments.

“The processor’s per-watt performance is also highly interesting in the context of mobile ultrasound devices and other battery-powered systems,” notes Maximilian Gerstl, Product Line Manager at congatec. “What Intel did with the architecture is very impressive. The numbers look great in terms of performance—not only on the CPU side, but also in terms of graphics. The new processors also offer an unprecedented level of flexibility to customers, allowing them to upgrade their systems across multiple generations while retaining the same form factor.”

“If there’s not a great new technology coming up, organizations will stay on the same module for 10 years or more so that they don’t have to recertify,” he continues. “Intel Core Ultra Mobile processors are a big step up. Healthcare organizations will have to think about changing to it.”

Open-Standard Modules Fast-Track System Upgrades

The latest congatec conga-TC700 COM Express Compact module incorporates the processing performance and application-ready AI capabilities of Intel Core Ultra Mobile processors in a plug-and-play form factor. Medical device designers can leverage the module as a shortcut to building efficient edge AI systems while significantly improving time-to-market and reducing total cost of ownership (TCO). And since COM Express is an open hardware standard governed by the global technology consortium, PICMG, the TC700 provides a vendor-neutral path to system upgrade whereby a legacy module can simply be swapped out for a higher-performance one with the same interfaces.

“The ability to quickly swap hardware means an organization can have its applications running for a very long time,” Gerstl explains. “Though they have to recertify new hardware components, they can bring over a lot of their software and hardware designs from previous applications.”

Intelligent Healthcare, Enabled by Edge AI Solutions

The conga-TC700 is supported by congatec’s OEM solution-focused ecosystem, which features efficient active and passive thermal management solutions, long-term support, and ready-to-use evaluation carrier boards. The company is also exploring how the open-source Intel® OpenVINO toolkit can empower its customers in the development and deployment of AI vision systems. According to Gerstl, the company is working on early benchmarking with specific use cases to help customers get their applications up and running more quickly.

For congatec, the availability of Intel Core Ultra Mobile processors represents a considerable step forward in the price, performance, and power consumption of next-generation edge AI devices. For medical device OEMs, these processors provide a compelling path to new, AI-enabled imaging and diagnostics equipment.

“We will continue to enable AI acceleration, hardware, and software and bring it to our products,” Gerstl says. “We want to enable this new trend.”

This article was edited by Georganne Benesch, Editorial Director for

About the Author

Brandon is a long-time contributor to going back to its days as Embedded Innovator, with more than a decade of high-tech journalism and media experience in previous roles as Editor-in-Chief of electronics engineering publication Embedded Computing Design, co-host of the Embedded Insiders podcast, and co-chair of live and virtual events such as Industrial IoT University at Sensors Expo and the IoT Device Security Conference. Brandon currently serves as marketing officer for electronic hardware standards organization, PICMG, where he helps evangelize the use of open standards-based technology. Brandon’s coverage focuses on artificial intelligence and machine learning, the Internet of Things, cybersecurity, embedded processors, edge computing, prototyping kits, and safety-critical systems, but extends to any topic of interest to the electronic design community. Drop him a line at, DM him on Twitter @techielew, or connect with him on LinkedIn.

Profile Photo of Brandon Lewis