Deep Learning Startup Brodmann17 Raises $11M For Automated Driving Tech

By NoCamels Team March 06, 2019 < 1 minute

Brodmann17 founders from left to right: Assaf Mushinsky, Amir Alush, and Adi Pinhas. Courtesy

Brodmann17, a software technology company that can run deep learning vision on edge devices, announced the closure of an $11 million investment round led by OurCrowd, with the participation of Maniv Mobility, AI Alliance, LLC, UL Ventures, Samsung NEXT, and the Sony Innovation Fund.

Founded in 2016 by entrepreneurs Adi Pinhas, Dr. Amir Alush, and Assaf Mushinsky, Brodmann17’s tech can be used in applications such as augmented reality, robotics, home security, smart cities, and autonomous vehicles.

In the autonomous driving field, Brodmann17 developed software that offers “game-changing deep learning perception technology” and provides “state-of-the-art accuracy while consuming a fraction of the computing power,” the company says.

Brodmann17 says its tech, developed by its team of top AI researchers and automotive experts, “is currently being tested by OEMs and Tier 1 suppliers worldwide, bringing ADAS (advanced driver-assistance systems) from the premium to the mass market to make roads safer.”

The startup’s “patent-pending software is able to run on the edge while increasing ADAS resolution, frame rate, and accuracy. The solution is easily integrated to quickly provide ADAS capabilities and meets the automotive industry’s toughest standards.”

“We’re excited that our strategic investors, many of whom are already leaders in the field of AI, have understood the game-changing ability of our innovative technology to bring automated driving to the mainstream on any hardware,” said Pinhas, Brodmann17’s CEO and co-founder, in a statement. “As the demand for ADAS rises, we will continue partnering with leading automotive industry stakeholders.”

Eli Nir, senior partner at OurCrowd, said: “We are convinced that Brodmann17 is one of the best deep AI companies out there. Brodmann17’s technology opens the doors for low computation implementation of AI – significantly lowering cost, complexity, and price, and can be used over many sectors and industries.”