Embedding deep neural networks at the push of a button

June 27, 2016 // By Julien Happich
With its 2nd generation neural network software framework CDNN2 (CEVA Deep Neural Network), CEVA promises to port the most demanding machine learning networks to its CEVA-XM4 vision processor at the push of a button.

CDNN2 enables localized, deep learning-based video analytics on camera devices in real time, significantly reducing data bandwidth and storage compared to running such analytics in the cloud, while lowering latency and increasing privacy.

A key component within the CDNN2 framework is the offline CEVA Network Generator, which converts a pre-trained neural network to an equivalent embedded-friendly network in fixed-point math at the push of a button. CDNN2 deliverables include a hardware-based development kit which allows developers to not only run their network in simulation, but also to run it with ease on the CEVA development board in real-time.

Improving on CEVA’s first generation neural network software framework (CDNN), CDNN2 adds support for TensorFlow, Google’s software library for machine learning, as well as offering improved capabilities and performance for the most sophisticated and latest network topologies and layers. It also supports fully convolutional networks, thereby allowing any given network to work with any input resolution.

Using a set of enhanced APIs, CDNN2 improves the overall system performance, including direct offload from the CPU to the CEVA-XM4 for various neural network-related tasks.

The CDNN2 software library is supplied as source code, extending the CEVA-XM4’s existing Application Developer Kit (ADK) and computer vision library, CEVA-CV.

It is flexible and modular, capable of supporting either complete CNN implementations or specific layers for a wide breadth of networks.

These networks include Alexnet, GoogLeNet, ResidualNet (ResNet), SegNet, VGG (VGG-19, VGG-16, VGG_S) and Network-in-network (NIN), among others.

CDNN2 supports the most advanced neural network layers including convolution, deconvolution, pooling, fully connected, softmax, concatenation and upsample, as well as various inception models. All network topologies are supported, including Multiple-Input-Multiple-Output, multiple layers per level, fully convolutional networks, in addition to linear networks (such as Alexnet).


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