Recogni's platform is orders of magnitude superior to anything we have seen. Further, this is a team we've known for years and have backed in the past. They are the right group to not only develop this promising technology but also get it into the hands of the auto OEMS.

GreatPoint VenturesLead investor

Autonomous systems are becoming smarter, driven by more powerful edge processing. The next opportunity is to achieve this higher machine intelligence at much lower power. We are excited by Recogni’s inference architecture for high-performance, low-power AI computing at the edge, and look forward to working with the team to build a world of safe and efficient autonomous systems.

Jim AdlerFounding Managing Director of Toyota AI Ventures

The ability to process sensor data on the edge efficiently and in real-time is essential in the development of autonomous vehicles. We believe that Recogni has the right approach and an experienced team to help solve these critical issues as the automotive industry continues on its path towards semi-autonomous and fully autonomous vehicles.

Marcus BehrendtPartner at BMW i Ventures

We truly believe in the sensor fusion based on Camera, RADAR and LIDAR, but computational requirements for those algorithms remains one of the critical bottlenecks in autonomous driving today. Recogni solves this problem with a unique and disruptive approach – we are proud to back this team of world-class IC and system developers, as well as automotive AI experts.

Sebastian StammInvestment Manager at Fluxunit – OSRAM Ventures

The issues within the Level 2+, 3, 4 and 5 autonomy ecosystem range from capturing/generating training data to inferring in real-time. These vehicles need datacenter class performance while consuming minuscule amounts of power. Leveraging our background in machine learning, computer vision, silicon, and system design, we are engineering a fundamentally new system that benefits the auto industry with very high efficiency at the lowest power consumption. This round, one of the largest initial venture rounds raised by any AI silicon company in the space, is testament to our experience and responsible approach.

RK AnandCEO of Recogni

The Problem is Speed, Power, & Visual Range

True driverless vehicles must analyze the environment, recognize objects at a distance, and make a decision in less than 50 milliseconds for urban driving and less than 30 milliseconds for highway driving.

Latency constraints require all image processing to be done within the car’s systems.

Cars have limited energy and power they can devote to the computational tasks without affecting range.

The Solution is Recogni’s Vision Cognition System

It’s the only multi-ocular camera system architecture purpose-built for object recognition that extracts passive stereoscopic depth at the pixel level.

Recogni achieves greater processing efficiency & speed by storing weights (parameters) of the object library on-chip, where the computational analysis is performed.

Recogni’s module is pipelined and operates at greater than 8Mpixel images at 60 frames per second, where it is able to recognize (detect, segment & classify) objects, fuse depth-sensor information into the objects, and provide the intelligence to the central system within 16 milliseconds for urban settings and 8 milliseconds for highway settings.

Just like humans, driverless vehicles need to understand their surroundings, localize themselves, and plan a path to travel. While RADAR, LiDAR, and Cameras are used to facilitate scene understanding, vehicle localization requires other sensors such as GPS, IMU, HD maps, and Wheel Odometry. Together, these sensors generate a tsunami of data that requires power efficient processing in realtime without reducing vehicle range.

Making fully autonomous vehicles a reality.

Our realtime object recognition system works faster and sees farther more power efficiently and accurately than any other system available today.

Check out our latest press release

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