The current deep learning system leverages advances in large computation power to define network, big data sets for training, and access to the large computing system to accomplish its goal.

Unfortunately, the efficient execution of this learning is not so easy on embedded systems (i.e. cars, drones and Internet of Things devices) whose processing power, memory size and bandwidth are usually limited.

This problem leaves wide open the possibility for innovation of technologies that can put deep neural network power into end devices.

"Deploying Artificial Intelligence at the edge [of the network] is becoming a massive trend," Movidius CEO, Remi El-Ouazzane, told us a few months ago.