Tech partnership looks to accelerate AI deployment in embedded systems

March 04, 2019 //By Wisse Hettinga
Tech partnership looks to accelerate AI deployment in embedded systems
Advantech, AMD, and Mentor, a Siemens business, are partnering together to make AI technology more accessible and easier to implement

The partnership is expected to create more AI-based business opportunities. AI technology will take embedded systems to the next level with higher efficiency and smarter systems designed to improve people’s lives.

For instance, diagnostic errors among the 300M diagnostic radiology images that are captured in the US contain errors of up to 4%. AI-infused image recognition using machine learning can see far more detail in MRI and X-ray images than human eyes, so it can improve diagnostic accuracy and help prioritise treatment.

AI technology in embedded system is often integrated in service model innovation rather than the manufactured product and it is highly sophisticated in the numerous tasks it performs including data collection, data analysis, pre-trained models, and inference. In terms of machine learning, programmed algorithms rely on powerful and reliable computing units for big data consumption. That’s why edge computing plays an important medium to satisfy the connection between the cloud and sensor devices.

With the partnership of Advantech, AMD, and Mentor, customers can accelerate AI implementation by integrating each party’s products and services. Advantech offers an embedded platform ( SOM-5871 /AIMB-228) equipped with the latest AMD Ryzen Embedded V1000 processer, in combination with the Mentor Embedded Linux operating system.

The AMD Ryzen Embedded V1000 processor supports frameworks, libraries, tools, and compilers for machine vision applications that leverage the full power of the Ryzen processor with Radeon Vega GPU technology. The three companies embrace open standards such as OpenVX, OpenCL API supported on the Linux kernel, so users can migrate machine learning across diverse hardware architectures for a variety of AI applications.

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