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Intel Releases “The Future of Avionics” White Paper

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Intel has collaborated with Daedalean, a Swiss startup that creates machine-learned solutions for the aviation industry. Their recent white paper presents a reference design for an AI application that acts as a never-distracted copilot, and is certifiable, meaning it meets regulatory tests. By releasing this white paper, Daedalean and Intel hope to provide guidance for other companies looking to integrate certifiable machine-learned electronics and applications into their aircraft.

Debra Aubrey is Technical Product Marketing Manager at Intel Corporation.

“The aviation industry still needs the first step towards a future with multidirectional embedded computational equipment: a reference architecture, or specific list of requirements to create the right types of computers,” she said. “A reference architecture encompasses regulatory requirements, low-level and high-level softwares, and silicon solutions for machine-learned applications. Regulators need to review a reference architecture to certify that it will create predictable, safe behavior in the sky.”

Daedalean has been working on a machine learning algorithm and a reference architecture for a computer capable of executing it. They tested the reference architecture in labs and on in-flight aircrafts to develop situational intelligence, the ability for machine-learned applications to predict and respond to future events. To make the time-to-market quicker for companies interested in their applications, Daedalean partnered with Intel, who provides silicon to manufacture these applications. The two companies collaborated on a reference architecture that speeds up the time-to-market, allowing companies to integrate machine-learned computers into their cockpits faster.

The white paper lays out the reference architecture for certifiable embedded electronics, including the challenges of applying software assurance to machine-learned devices, the visual awareness system they utilize, and the current and future role of embedded computing in the industry. The report also looks at the software and hardware requirements that ensure aviation systems are safe and effective.

According to a statement provided by Intel and Daedalean, the reference architecture “can significantly reduce time-to-market for companies interested in incorporating what they have coined situational intelligence—the ability not only to understand and make sense of the current environment and situation but also anticipate and react to a future situation—in the cockpit.”

Dr. Niels Haandbaek is Director of Engineering at Daedalean.

“This is the first document ever to present a real-world working example and provide guidance on how to approach the challenges of implementing the machine learning application in airworthy embedded systems in general: how to ensure that your ML-based system can meet the computational requirements, certification requirements, and the size, weight, and power (SWaP) limitations at the same time. The approach described in the document is driving the aviation industry’s need for high-performance embedded computing,” he said.

This white paper can help bring the power of AI to avionics. It is the first document to present a working example of a machine-learned system and to provide guidance about how to overcome application challenges. The actionable recommendations and findings in the new report can drive the industry’s desire for high-performance embedded computing. This foundational real-world example has the potential to cultivate a new wave of airworthy machine-learned applications.

You can download the white paper here.

Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.