Mountain View, CA. CEVA Inc., a licensor of signal-processing IP for smarter, connected devices, and Brodmann17, a developer of patent-pending deep-learning technology, have announced a partnership aimed at accelerating the deployment of deep-learning computer vision in mainstream applications. Through the partnership, CEVA and Brodmann17 said they would bring an order-of-magnitude increase in performance and power efficiency for deep-learning in edge devices compared with leading GPU-based implementations.
The push towards widespread adoption of AI in consumer devices continues at a relentless pace. However, cloud-based deep learning on battery-powered devices is plagued with issues, including latency, security, and the need for a constant, reliable Internet connection. Implementing the intelligence on the device itself—or on the edge—eliminates all of these issues. Highly efficient computer-vision processors are necessary to meet the stringent power requirements, and specialized deep-learning software is crucial in delivering the accuracy and performance needed for cloud-based systems.
Targeting embedded devices, Brodmann17 has developed a specialized deep-learning technology for visual recognition aimed at edge-based artificial intelligence. Using patent-pending techniques, Brodmann17’s deep-learning architecture generates smaller neural networks that are faster and more accurate than any other network generated on the market, the company said. Through the collaboration with Brodmann17, licensees of the CEVA-XM platforms and their customers will be able to use Brodmann17’s deep-learning object detection that achieves state-of-the-art accuracy on the CEVA-XM at a rate of 100 frames per second.
“Our patent-pending deep-learning vision software is a perfect fit for the many CEVA customers and OEMs using CEVA-XM platforms to add intelligence to their devices,” said Adi Pinhas, CEO of Brodmann17. “This first-of-its-kind combination of hardware and software achieves real-time performance that supports multicameras with a single DSP or higher resolutions.”
“To truly maximize the performance and capabilities of AI, in mass-market devices, it requires not just application-specific hardware like our CEVA-XM platforms, but also neural networks that are trained to be run efficiently on the edge-embedded devices,” said IlanYona, vice president of the vision business unit at CEVA. “Brodmann17’s deep-learning software provides the capability to create extremely light, accurate, and flexible networks, trained from the ground up with embedded in mind. We’re delighted to partner with them and bring their unique capabilities to the CEVA-XM ecosystem.”
Brodmann17 will present and demonstrate their deep learning software together with the CEVA-XM4 imaging and vision platform at the CEVA Technology Symposium Series in China and Taiwan, October 23 to October 27, 2017. Visitors to the symposium can meet computer-vision experts from CEVA and Brodmann17 at these events.