Partners Plan To Boost AI Performance Of Edge Devices

CEVA and Broadmann17 are partnering with the goal of accelerating artificial intelligence (AI) via deployment of deep learning computer vision in mainstream applications. The partnership is expected to 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 for adoption of AI in consumer devices is rising, but cloud-based deep learning on battery-powered devices is plagued with latency, security and internet connection issues. 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.

Free Newsletter

Like this article? Subscribe to FierceSensors!

The sensors industry is constantly changing as innovation runs the market’s trends. FierceSensors subscribers rely on our suite of newsletters as their must-read source for the latest news, developments and analysis impacting their world. Register today to get sensors news and updates delivered right to your inbox.

 

Targeting embedded devices, Brodmann17 has developed a specialized deep learning technology for visual recognition aimed at edge-based artificial intelligence. Using patent-pending techniques, the company’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.

 

More details and info are available from Brodmann17 and CEVA.

Suggested Articles

A motion sensor (or motion detector) is an electronic device that is designed to detect and measure movement

Analyst praises Intel’s AI investments

The global mini LED market is projected to reach $5.9 billion by 2025, according to Grand View Research, Inc.