Search Results for author: Zhi Zhou

Found 29 papers, 9 papers with code

Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs

1 code implementation16 Dec 2023 Aodong Chen, Fei Xu, Li Han, Yuan Dong, Li Chen, Zhi Zhou, Fangming Liu

GPUs have become the defacto hardware devices to accelerate Deep Neural Network (DNN) inference in deep learning(DL) frameworks.


DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset

no code implementations20 Oct 2023 Weijie Liu, Xiaoxi Zhang, Jingpu Duan, Carlee Joe-Wong, Zhi Zhou, Xu Chen

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data.

Federated Learning Navigate

Parameter-Efficient Long-Tailed Recognition

1 code implementation18 Sep 2023 Jiang-Xin Shi, Tong Wei, Zhi Zhou, Xin-Yan Han, Jie-Jing Shao, Yu-Feng Li

In this paper, we propose PEL, a fine-tuning method that can effectively adapt pre-trained models to long-tailed recognition tasks in fewer than 20 epochs without the need for extra data.

 Ranked #1 on Long-tail Learning on CIFAR-100-LT (ρ=10) (using extra training data)

Fine-Grained Image Classification Long-tail learning with class descriptors

Solving Elliptic Optimal Control Problems using Physics Informed Neural Networks

no code implementations23 Aug 2023 Bangti Jin, Ramesh Sau, Luowei Yin, Zhi Zhou

In this work, we present and analyze a numerical solver for optimal control problems (without / with box constraint) for linear and semilinear second-order elliptic problems.

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

no code implementations4 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.


Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets

no code implementations22 Apr 2023 Huirong Ma, Zhi Zhou, Xiaoxi Zhang, Xu Chen

Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs.


Electrical Impedance Tomography with Deep Calderón Method

1 code implementation18 Apr 2023 Siyu Cen, Bangti Jin, Kwancheol Shin, Zhi Zhou

Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject.

Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks

1 code implementation29 Mar 2023 Bangti Jin, Xiyao Li, Qimeng Quan, Zhi Zhou

In this work we develop a novel approach using deep neural networks to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain.

HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association

no code implementations16 Jan 2023 Qiong Wu, Xu Chen, Tao Ouyang, Zhi Zhou, Xiaoxi Zhang, Shusen Yang, Junshan Zhang

Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally.

Edge-computing Federated Learning

GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers

no code implementations31 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.

Miscellaneous Scheduling

Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method

1 code implementation7 Sep 2022 Tianhao Hu, Bangti Jin, Zhi Zhou

Extensive numerical experiments in two- and multi-dimensional spaces with point sources, line sources or their combinations are presented to illustrate the efficiency of the proposed approach, and a comparative study with several existing approaches based on neural networks is also given, which shows clearly its competitiveness for the specific class of problems.

LAMDA-SSL: Semi-Supervised Learning in Python

1 code implementation9 Aug 2022 Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li

The second part shows the usage of LAMDA-SSL by abundant examples in detail.

Robust Deep Semi-Supervised Learning: A Brief Introduction

no code implementations12 Feb 2022 Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li

Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient.

Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

no code implementations25 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.

Edge-computing Simultaneous Localization and Mapping

STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data

no code implementations NeurIPS 2021 Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yu-Feng Li, ShiLiang Pu

However, in many real-world applications, it is desirable to have SSL algorithms that not only classify the samples drawn from the same distribution of labeled data but also detect out-of-distribution (OOD) samples drawn from an unknown distribution.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

no code implementations21 Jan 2021 Qiong Wu, Xu Chen, Zhi Zhou, Liang Chen, Junshan Zhang

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity.

FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring

1 code implementation14 Dec 2020 Qiong Wu, Xu Chen, Zhi Zhou, Junshan Zhang

In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally.

Human Activity Recognition Personalized Federated Learning

CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices

no code implementations6 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.

When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network

no code implementations22 Sep 2020 Shuai Yu, Xu Chen, Zhi Zhou, Xiaowen Gong, Di wu

Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e. g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes.

Decision Making Edge-computing +2

The energy technique for the six-step BDF method

no code implementations17 Jul 2020 Georgios Akrivis, Minghua Chen, Fan Yu, Zhi Zhou

In combination with the Grenander--Szeg\"o theorem, we observe that a relaxed positivity condition on multipliers, milder than the basic %fundamental requirement of the Nevanlinna--Odeh multipliers that the sum of the absolute values of their components is strictly less than $1$, makes the energy technique applicable to the stability analysis of BDF methods for parabolic equations with selfadjoint elliptic part.

Numerical Analysis Numerical Analysis

HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing

no code implementations22 Mar 2020 Deyin Liu, Xu Chen, Zhi Zhou, Qing Ling

We develop a novel \textit{hybrid parallelism} method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center.

Cloud Computing Scheduling

DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network

no code implementations9 Mar 2020 Qiong Wu, Muhong Wu, Xu Chen, Zhi Zhou, Kaiwen He, Liang Chen

Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE.

Decision Making Generative Adversarial Network

HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

no code implementations26 Feb 2020 Siqi Luo, Xu Chen, Qiong Wu, Zhi Zhou, Shuai Yu

We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization.

Distributed, Parallel, and Cluster Computing

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

no code implementations4 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.

Change Point Detection Collaborative Inference +1

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

no code implementations14 Sep 2018 Tao Ouyang, Zhi Zhou, Xu Chen

To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint.

Cloud Computing Edge-computing

Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy

no code implementations20 Jun 2018 En Li, Zhi Zhou, Xu Chen

As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight.

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