  As a key technology of enabling Artificial Intelligence (AI) applications in
5G era, Deep Neural Networks (DNNs) have quickly attracted widespread
attention. However, it is challenging to run computation-intensive DNN-based
tasks on mobile devices due to the limited computation resources. What's worse,
traditional cloud-assisted DNN inference is heavily hindered by the significant
wide-area network latency, leading to poor real-time performance as well as low
quality of user experience. To address these challenges, in this paper, we
propose Edgent, a framework that leverages edge computing for DNN collaborative
inference through device-edge synergy. Edgent exploits two design knobs: (1)
DNN partitioning that adaptively partitions computation between device and edge
for purpose of coordinating the powerful cloud resource and the proximal edge
resource for real-time DNN inference; (2) DNN right-sizing that further reduces
computing latency via early exiting inference at an appropriate intermediate
DNN layer. In addition, considering the potential network fluctuation in
real-world deployment, Edgentis properly design to specialize for both static
and dynamic network environment. Specifically, in a static environment where
the bandwidth changes slowly, Edgent derives the best configurations with the
assist of regression-based prediction models, while in a dynamic environment
where the bandwidth varies dramatically, Edgent generates the best execution
plan through the online change point detection algorithm that maps the current
bandwidth state to the optimal configuration. We implement Edgent prototype
based on the Raspberry Pi and the desktop PC and the extensive experimental
evaluations demonstrate Edgent's effectiveness in enabling on-demand
low-latency edge intelligence.
