  Recent studies have shown the latency and energy consumption of deep neural
networks can be significantly improved by splitting the network between the
mobile device and cloud. This paper introduces a new deep learning
architecture, called BottleNet, for reducing the feature size needed to be sent
to the cloud. Furthermore, we propose a training method for compensating for
the potential accuracy loss due to the lossy compression of features before
transmitting them to the cloud. BottleNet achieves on average 30x improvement
in end-to-end latency and 40x improvement in mobile energy consumption compared
to the cloud-only approach with negligible accuracy loss.
