  Emerging Internet of Things (IoT) and mobile computing applications are
expected to support latency-sensitive deep neural network (DNN) workloads. To
realize this vision, the Internet is evolving towards an edge-computing
architecture, where computing infrastructure is located closer to the end
device to help achieve low latency. However, edge computing may have limited
resources compared to cloud environments and thus, cannot run large DNN models
that often have high accuracy. In this work, we develop REACT, a framework that
leverages cloud resources to execute large DNN models with higher accuracy to
improve the accuracy of models running on edge devices. To do so, we propose a
novel edge-cloud fusion algorithm that fuses edge and cloud predictions,
achieving low latency and high accuracy. We extensively evaluate our approach
and show that our approach can significantly improve the accuracy compared to
baseline approaches. We focus specifically on object detection in videos
(applicable in many video analytics scenarios) and show that the fused
edge-cloud predictions can outperform the accuracy of edge-only and cloud-only
scenarios by as much as 50%. We also show that REACT can achieve good
performance across tradeoff points by choosing a wide range of system
parameters to satisfy use-case specific constraints, such as limited network
bandwidth or GPU cycles.

Computer Vision and Pattern Recognition;Distributed, Parallel, and Cluster Computing;Multimedia