Enabling Incremental Knowledge Transfer for Object Detection at the Edge
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.
Cite
@article{arxiv.2004.05746,
title = {Enabling Incremental Knowledge Transfer for Object Detection at the Edge},
author = {Mohammad Farhadi Bajestani and Mehdi Ghasemi and Sarma Vrudhula and Yezhou Yang},
journal= {arXiv preprint arXiv:2004.05746},
year = {2020}
}
Comments
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW)