CDNN is a comprehensive toolkit that simplifies the development and deployment of deep learning systems for mass-market embedded devices. Tailored and optimized for the CEVA-XM family of imaging and vision DSPs, the CDNN toolkit includes the CEVA network generator, the CDNN software framework and a CNN hardware accelerator that works in tandem to deliver superior performance while ensuring flexibility with the constantly evolving requirements of machine learning.
Separately, each component of the CDNN toolkit is a powerful enabler of embedded imaging & vision applications. Combined, these pieces deliver an ultimate toolkit to support new network structures and changing layer types of deep neural networks.
CEVA supplies a full development platform for partners and developers based on the CEVA-XM family of DSP cores to enable the development of deep learning applications using the CDNN, targeting any advanced network.
The CDNN toolkit streamlines implementations of deep learning in embedded systems by automatically converting offline pre-trained neural networks to real-time embedded-ready networks for CEVA-XM cores, utilizing the CEVA Network Generator. This enables real-time high quality image classification, object recognition and vision analytics.
- CEVA Network Generator converts pre-trained neural network model and weights from offline training framework, such as Caffe or TensorFlow, to a real-time network model.
- CDNN2 second generation neural network software framework accelerates deployment of machine learning in low power embedded systems.
- CNN hardware accelerator delivering 512 MACs/cycle.
- Coupled with the CEVA-XM family of intelligent vision processors, CDNN offers significant time-to-market and power advantages for implementing machine learning in embedded systems.
CEVA CDNN2 live demonstration
CDNN2 is CEVA’s 2nd Generation Neural Network Software Framework for Machine Learning that supports Artificial Intelligence Including Google’s TensorFlow
CDNN2 enables localized, deep learning-based video analytics on camera devices in real time. Coupled with the CEVA-XM intelligent vision processor family, CDNN2 offers significant time-to-market and power advantages for implementing machine learning in embedded systems for smartphones, advanced driver assistance systems (ADAS), surveillance equipment, drones, robots and other camera-enabled smart devices.
In this video clip we demonstrate how a pre-trained neural network (“Age Classification”, can be found here: http://www.openu.ac.il/home/hassner/p…) is downloaded from the internet, converted to an embedded-friendly network, and runs on the CEVA-XM4 FPGA platform in less than 10 mins!