DeepLite: Real-Time Deep Learning Framework for Neighborhood Analysis

In this paper, we propose a new framework, DeepLite, for real-time deep learning on the edge. In DeepLite, a network of multiple deep learning models is designed to conduct the context-aware inferencing using real-time deep learning technologies. Comprehensively, DeepLite has several innovative concepts as follows: 1) an inference network of deep learning models/containers, 2) invocation of a new model based on the output of the previous model's inferencing, 3) intelligent containers for models, 4) plug-and-play model/container, and 5) microservice architecture on the edge. DeepLite has been evaluated via a case study, NeighborNets which was based on an inference network of several deep learning models deployed to edge devices for computer vision, e.g., object detection algorithms to compute a diverse range of aspects, such as house types, level of greenery, house age, traffic conditions, and the types of recreational facilities.