no code implementations • 27 Apr 2024 • Liekang Zeng, Shengyuan Ye, Xu Chen, Yang Yang
Motivated by this, in this article, we propose collaborative edge training, a novel training mechanism that orchestrates a group of trusted edge devices as a resource pool for expedited, sustainable big AI model training at the edge.
no code implementations • 19 Jul 2023 • Liekang Zeng, Haowei Chen, Daipeng Feng, Xiaoxi Zhang, Xu Chen
Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones.
no code implementations • 4 Jul 2023 • Liekang Zeng, Xu Chen, Peng Huang, Ke Luo, Xiaoxi Zhang, Zhi Zhou
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.
no code implementations • 20 Jan 2023 • Hao Wang, Hao Bao, Liekang Zeng, Ke Luo, Xu Chen
To identify dense and small-size pedestrians in surveillance systems, high-resolution cameras are widely deployed, where high-resolution images are captured and delivered to off-the-shelf pedestrian detection models.
no code implementations • 31 Oct 2022 • Liekang Zeng, Chongyu Yang, Peng Huang, Zhi Zhou, Shuai Yu, Xu Chen
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques.
no code implementations • 25 Dec 2021 • Peng Huang, Liekang Zeng, Xu Chen, Ke Luo, Zhi Zhou, Shuai Yu
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community.
no code implementations • 6 Dec 2020 • Liekang Zeng, Xu Chen, Zhi Zhou, Lei Yang, Junshan Zhang
CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions.
no code implementations • 15 Jul 2020 • Xin Tang, Xu Chen, Liekang Zeng, Shuai Yu, Lin Chen
With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.
no code implementations • 9 Apr 2020 • Haowei Chen, Liekang Zeng, Shuai Yu, Xu Chen
In this article, we propose an edge computation offloading framework based on Deep Imitation Learning (DIL) and Knowledge Distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computation tasks online.
no code implementations • 4 Oct 2019 • En Li, Liekang Zeng, Zhi Zhou, Xu Chen
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention.
no code implementations • 24 May 2019 • Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang
To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence.