Delivering Deep Learning to Mobile Devices via Offloading
Xukan Ran, Haoliang Chen, Zhenming Liu, Jiasi Chen · 2017
Deep learning has the potential to make Augmented Reality (AR) devices smarter, but few AR apps use such technology today because it is compute-intensive, and front-end devices cannot deliver sufficient compute power. We propose a distributed framework that ties together front-end devices with more powerful back-end "helpers" that allow deep learning to be executed locally or to be offloaded. This framework should be able to intelligently use current estimates of network conditions and back-end server loads, in conjunction with the application's requirements, to determine an optimal strategy.