Coupled with the
CEVA-XM4 intelligent vision processor, CDNN2:
Offers significant time-to-market advantages for implementing machine learning in embedded systems – saves months in the deployment of DNNs onto real-life constraints.
Significantly improves on power consumption and memory bandwidth compared to a leading GPU-base system.
Supports any given layer in any network topology, on any resolution, as trained by Caffe and TensorFlow. Supported networks including; Alexnet, GoogLeNet, ResidualNet (ResNet), SegNet, VGG and Network-in-network (NIN).