31 Oct 2017

CEVA & Brodmann17 Partner to Develop AI Technology

CEVA and Broadmann17, have announced a partnership aimed at accelerating the deployment of deep learning computer vision in mainstream applications. Through the partnership, CEVA and Brodmann17 bring an order of magnitude increase in performance and power efficiency for deep learning in edge devices compared to the leading GPU-based implementations.

The push towards widespread adoption of Artificial Intelligence, 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. 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. This equates to 170% better performance than the same software running on the NVIDIA Jetson TX2 AI Supercomputer. Comparing to the popular combination today of Faster-RCNN algorithm over NVIDIA TX2 it is an improvement of 20 times (2000%) in 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 multi-cameras 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 Ilan Yona, 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.”

Most popular news in Circuit Design

Marvell opens CISPR25 automotive EMC laboratory
Firefighting robots create 3D thermal images for rescuers
LabVIEW 2015 provides faster coding & operation
RS and Allied ship 512MB Raspberry Pi boards
Researchers edge closer to large quantum computer

All news in this channel | All news


Share this page


Want more like this? Register for our newsletter






Object Recognition with 3D Time-of-Flight Cameras and Neural Networks Mark Patrick | Mouser Electronics
Object Recognition with 3D Time-of-Flight Cameras and Neural Networks
Machine vision - the ability for computers to see and recognise the world around us - is becoming more important for a variety of fields, from IoT and manufacturing through to augmented reality.









Radio-Electronics.com is operated and owned by Adrio Communications Ltd and edited by Ian Poole. All information is © Adrio Communications Ltd and may not be copied except for individual personal use. This includes copying material in whatever form into website pages. While every effort is made to ensure the accuracy of the information on Radio-Electronics.com, no liability is accepted for any consequences of using it. This site uses cookies. By using this site, these terms including the use of cookies are accepted. More explanation can be found in our Privacy Policy