activity
20232026
most citedEmpowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models

185 citations · 193 across the 12 of their papers we have counts for

collaborators
Showing cs.DCShow all

5 papers · 1 filter

cs.DC2026

LASER: Load-Aware Serving with Early-Exit for Reasoning LLMs at the Edge

Zhiqing Tang, Size Li, Hanshuai Cui +5

Large reasoning models (LRMs) such as DeepSeek-R1 have achieved strong performance through extended chain-of-thought (CoT) generation. However, deploying them on edge devices raise…

cs.DC2025★ 2 cited

LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing

Zhiqing Tang, Wentao Peng, Jianxiong Guo +5

Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further…

cs.DC2025★ 2 cited

Adaptive Configuration Selection for Multi-Model Inference Pipelines in Edge Computing

Jinhao Sheng, Zhiqing Tang, Jianxiong Guo +1

The growing demand for real-time processing tasks is driving the need for multi-model inference pipelines on edge devices. However, cost-effectively deploying these pipelines while…

cs.DC2024

Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing

Mengjie Liu, Yihua Li, Fangyi Mou +4

Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security an…

cs.DC2023

Joint Task Scheduling and Container Image Caching in Edge Computing

Fangyi Mou, Zhiqing Tang, Jiong Lou +3

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that…