Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services

M Xu, H Du, D Niyato, J Kang, Z Xiong… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …

Distributed artificial intelligence empowered by end-edge-cloud computing: A survey

S Duan, D Wang, J Ren, F Lyu, Y Zhang… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Convergence of edge computing and deep learning: A comprehensive survey

X Wang, Y Han, VCM Leung, D Niyato… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

Efficient acceleration of deep learning inference on resource-constrained edge devices: A review

MMH Shuvo, SK Islam, J Cheng… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Edge intelligence: Empowering intelligence to the edge of network

D Xu, T Li, Y Li, X Su, S Tarkoma, T Jiang… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Deep learning in mobile and wireless networking: A survey

C Zhang, P Patras, H Haddadi - IEEE Communications surveys …, 2019 - ieeexplore.ieee.org
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Patdnn: Achieving real-time dnn execution on mobile devices with pattern-based weight pruning

W Niu, X Ma, S Lin, S Wang, X Qian, X Lin… - Proceedings of the …, 2020 - dl.acm.org
With the emergence of a spectrum of high-end mobile devices, many applications that
formerly required desktop-level computation capability are being transferred to these …

Elf: accelerate high-resolution mobile deep vision with content-aware parallel offloading

W Zhang, Z He, L Liu, Z Jia, Y Liu, M Gruteser… - Proceedings of the 27th …, 2021 - dl.acm.org
As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …

Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices

X Ma, FM Guo, W Niu, X Lin, J Tang, K Ma… - Proceedings of the …, 2020 - ojs.aaai.org
Abstract Model compression techniques on Deep Neural Network (DNN) have been widely
acknowledged as an effective way to achieve acceleration on a variety of platforms, and …

A survey on deep neural network compression: Challenges, overview, and solutions

R Mishra, HP Gupta, T Dutta - arXiv preprint arXiv:2010.03954, 2020 - arxiv.org
Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …