Did you miss a session from the Future of Work Summit? Head over to our Future of Work Summit on-demand library to stream.
Recogni, a startup designing an AI-powered vision recognition module for autonomous vehicles, today announced it raised $48.9 million. The company says the funds will help it bring its perception product to market while expanding the size of its engineering and go-to-market teams.
CEO RK Anand believes that self-driving cars have a compute problem. While the models that guide the cars’ decision-making have powerful servers for training, inferencing — the stage at which the algorithms make predictions — must be performed offline to ensure redundancy. But even fully trained models require in-car PCs for real-time processing, a paradigm some assert is unsustainable.
That’s why in 2017, Anand cofounded San Jose, California-based Recogni with Ashwini Choudhary, Eye-Fi founder Eugene Feinberg, former Lilium sensor systems engineer Gilles Backhus, and Valerie Chan. Recogni’s integrated module, which comprises passively cooled image sensors, an external depth sensor, and a custom inferencing chip, can perform up to 1 peta operations per second while consuming about 8 watts of power. Thanks to an approach that offloads central processing to multiple points on a vehicle, the chip can capture and analyze up to three uncompressed 8-12 MP streams at 60 frames per second, achieving 70% compute efficiency in typical vision applications.
Recogni claims its system outperforms rivals by more than two orders of magnitude on perception tasks like image classification, objection detection, action anticipation, and depth inference. On the popular benchmark ResNet 50, the module is able to classify 92,105 images per second; on RetinaNet-101-800, it performs 1,750 inferences per second; and on R(2+1)D, it can spot 833 people concurrently.
“Recogni’s AI vision cognition module is designed to capture and process high-resolution camera and sensor data,” a spokesperson told VentureBeat via email. “Applying innovations in AI algorithms, ASIC architecture and design, and system software, the [module] will deliver high performance processing with ultra-low power consumption, enabling high-resolution, high-frame-rate image processing from multiple cameras concurrently in real time.”
Recogni intends to initially target level 2 autonomous vehicles as defined by the Society of Automotive Engineers, which includes those equipped with advanced driver assistance systems like Cadillac’s Super Cruise, Nvidia’s Drive AutoPilot, and Volvo’s Pilot Assist. In the future, the company plans to pivot to platforms for level 3 vehicles that can steer, accelerate or decelerate, and pass other cars without human input and level 4 cars that can largely drive themselves without human intervention, with the goal of eventually enabling cars that can do anything a human driver can do.
Strictly vision-based approaches to autonomous driving are by no stretch universally lauded, but they’re advocated by Intel’s Mobileye, which is developing a custom accelerator processor chip that offers 360-degree coverage, courtesy of proprietary algorithms, cameras, and ultrasonic. Similarly, driverless semi-truck startup TuSimple says its camera-based technology, which employs lidar largely for redundancy, has a 1,000-meter detection range. And Beijing tech giant Baidu recently debuted a vision-based vehicle framework, Apollo Lite, that it claims has demonstrated full autonomy on public roads.
Recogni has a competitor in Tesla as well, which in April 2019 detailed a Samsung-manufactured chipset featuring over 144 tera operations per second of AI performance. Nvidia, another formidable rival in the space, says that Drive Xavier, the system-on-chip at the heart of its Drive AGX Pegasus autonomous vehicle development platform, draws just 30 watts.
But Recogni is no fly-by-night operation. Anand has confidence its pedigreed roster of engineers, managers, and data scientists, who hail from Intel, Juniper Networks, Kumu Networks, Sun Microsystems, Silicon Graphics, Xsigo Systems, NavVis, and Cisco, among others, have the chops to take on the industry’s best-funded incumbents.
“This investment is a strong endorsement of Recogni’s vision from venture and industry leaders,” Anand said. He added that while Recogni hasn’t yet begun mass-producing its module, the company has completed proofs of concept with “several” unnamed automotive customers. “We are moving along our journey of solving the challenge of perception processing and power efficiency by building the world’s highest performing AI Inference system at the lowest energy consumption.”
WRVI Capital, Mayfield Fund, Continental Automobiles, Robert Bosch Venture Capital, and existing investors GreatPoint Ventures, Toyota AI Ventures, BMW i Ventures, Fluxunit-OSRAM Ventures, DNS Capital, and others participated in Recogni’s series B funding round. It brings the company’s total raised to date to over $65 million following a $25 million round in July 2019.
Beyond San Jose, Recogni has operations in Munich and its Cupertino headquarters.
VentureBeatVentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:
- up-to-date information on the subjects of interest to you
- our newsletters
- gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
- networking features, and more