Mobile deep learning requires ultra-efficiency

The successful spread of artificial intelligence (AI) into everyday applications will be dependent on how easy it is to deploy deep neural networks in small, low-power devices rather than large server networks

In this post we look at ways to deal with those challenges.

Googlenet deep convolutional neural network

In 2014, Google made an entry to the ImageNet large-scale visual recognition challenge (ILSVRC), titled GoogLeNet. It is an interesting case study because it is a 22-layer deep convolutional network, and includes nine inceptions, creating a very rich and complex topology.

In the GoogLeNet network, each connection in each layer can potentially go back and forth through DDR. To handle this in an embedded system poses a challenge. The complex topology of the network must be divided into batches of layers to run on a DSP or dedicated hardware. We call this subnetwork division.

In our CEVA network generator tool, all analysis is done automatically without user intervention. The network is divided into subnetworks and each subnetwork runs on the DSP according to the execution order set by the network generator. For example, let’s take a look at the inception part of the GoogLeNet network after going through our network generator tool.

Read the full article published in Radio-Electronics

#CDNN #CNN #Deep Learning #Deep Learning; Face Recognition #Deep Neural Network