  Modern mobile devices are equipped with high-performance hardware resources
such as graphics processing units (GPUs), making the end-side intelligent
services more feasible. Even recently, specialized silicons as neural engines
are being used for mobile devices. However, most mobile devices are still not
capable of performing real-time inference using very deep models. Computations
associated with deep models for today's intelligent applications are typically
performed solely on the cloud. This cloud-only approach requires significant
amounts of raw data to be uploaded to the cloud over the mobile wireless
network and imposes considerable computational and communication load on the
cloud server. Recent studies have shown that the latency and energy consumption
of deep neural networks in mobile applications can be notably reduced by
splitting the workload between the mobile device and the cloud. In this
approach, referred to as collaborative intelligence, intermediate features
computed on the mobile device are offloaded to the cloud instead of the raw
input data of the network, reducing the size of the data needed to be sent to
the cloud. In this paper, we design a new collaborative intelligence friendly
architecture by introducing a unit responsible for reducing the size of the
feature data needed to be offloaded to the cloud to a greater extent, where
this unit is placed after a selected layer of a deep model. Our proposed
method, across different wireless networks, achieves on average 53x
improvements for end-to-end latency and 68x improvements for mobile energy
consumption compared to the status quo cloud-only approach for ResNet-50, while
the accuracy loss is less than 2%.

Distributed, Parallel, and Cluster Computing