Deep learning on mobile: The how-to guide

Description

Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in the area of computer vision. Anirudh Koul explains how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones, wearable devices, and drones.

Many mobile applications running on smartphones and wearable devices would potentially benefit from the new opportunities enabled by deep learning techniques. Local execution allows data to stay on the mobile device, avoiding latency issues entailed by data transmission to the cloud and alleviating privacy concerns. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Anirudh shares various strategies to circumvent these obstacles and build mobile-friendly shallow CNN architectures that significantly reduce the memory footprint and therefore make them easier to store on a smartphone; he also explains how to use a family of model compression techniques to prune the network size for live image processing, enabling you to build a CNN version optimized for inference on mobile devices. Along the way, he outlines practical strategies to preprocess your data in a manner that makes the models more efficient in the real world.

Anirudh showcases these techniques using a real-world project and discusses tips and tricks, speed and accuracy trade-offs, and benchmarks on different hardware to demonstrate how to get started developing your own deep learning application suitable for deployment on storage- and power-constrained mobile devices. You can also apply similar techniques to make deep neural nets more efficient when deploying in a regular cloud-based production environment, thus reducing the number of GPUs required and optimizing on cost.