Untether AI Joins SOAFEE Special Interest Group to Drive Energy-Centric AI Acceleration Solutions into Software-Defined Vehicles

Untether AI announced that it has joined the Scalable Open Architecture for Embedded Edge Special Interest Group. The SOAFEE SIG is focused on delivering an architecture for automotive applications with associated reference implementations to help advance the deployment of software-defined vehicles. Untether AI’s joining the organization represents the company's ongoing commitment to the automotive marketplace. Untether AI’s expertise in AI inference acceleration for advanced driver-assistance systems and autonomous vehicle applications will be invaluable for furthering the organization's goals.

“As the industry leader in energy-centric AI inference acceleration, we are excited about contributing to and engaging with SOAFEE SIG,” said Robert Beachler, vice president of product at Untether AI. “The clear benefits of SDV and the associated demands for best-in-class AI acceleration performance with predictable, low-latency responsiveness makes SOAFEE membership a natural fit for Untether AI. We look forward to collaborating closely with the SOAFEE ecosystem to provide our unique AI insights to the SIG to help usher in this transformative era in the automotive industry.”

More than 100 companies are members of the SOAFEE SIG, including major automotive OEMs, Tier 1s, semiconductor suppliers, IP suppliers, and software suppliers. The concept of the software-defined vehicle promises to lower development costs and enhance platform scalability by revolutionizing how vehicles are designed, built, and maintained. The key to platform scaling for SDVs lies in a centralized architecture. This drives the need for both high-performance and energy-centric AI acceleration. With today’s connected car, this requirement extends beyond the vehicle itself to encompass cloud services, creating a significant market for AI acceleration solutions tailored for automotive use.

Source: Untether AI media announcement