Through the partnership, the two companies aim 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 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, though highly efficient computer vision processors are necessary to meet the stringent power requirements of edge devices 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, the deep learning architecture generates smaller neural-networks that are faster and more accurate than any other network generated on the market, claims Brodmann17. 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 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 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