Enabling Incremental Knowledge Transfer for Object Detection at the Edge

TL;DR

This paper presents a system that enables resource-efficient object detection on edge devices by using a shallow neural network and transferring knowledge from a deep neural network to adapt to environmental changes, significantly reducing energy and inference time.

Contribution

It introduces a novel system-level design that combines a shallow neural network with a knowledge transfer mechanism from a deep neural network for edge object detection.

Findings

Energy consumption reduced by 78%

Inference time decreased by 71%

Effective knowledge transfer improves detection adaptability

Abstract

Object detection using deep neural networks (DNNs) involves a huge amount of computation which impedes its implementation on resource/energy-limited user-end devices. The reason for the success of DNNs is due to having knowledge over all different domains of observed environments. However, we need a limited knowledge of the observed environment at inference time which can be learned using a shallow neural network (SHNN). In this paper, a system-level design is proposed to improve the energy consumption of object detection on the user-end device. An SHNN is deployed on the user-end device to detect objects in the observing environment. Also, a knowledge transfer mechanism is implemented to update the SHNN model using the DNN knowledge when there is a change in the object domain. DNN knowledge can be obtained from a powerful edge device connected to the user-end device through LAN or Wi-Fi. Experiments demonstrate that the energy consumption of the user-end device and the inference time can be improved by 78% and 71% compared with running the deep model on the user-end device.