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 …

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 …

[图书][B] Efficient processing of deep neural networks

V Sze, YH Chen, TJ Yang, JS Emer - 2020 - Springer
This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

Orbital edge computing: Nanosatellite constellations as a new class of computer system

B Denby, B Lucia - Proceedings of the Twenty-Fifth International …, 2020 - dl.acm.org
Advances in nanosatellite technology and a declining cost of access to space have fostered
an emergence of large constellations of sensor-equipped satellites in low-Earth orbit. Many …

Deepcache: Principled cache for mobile deep vision

M Xu, M Zhu, Y Liu, FX Lin, X Liu - Proceedings of the 24th annual …, 2018 - dl.acm.org
We present DeepCache, a principled cache design for deep learning inference in
continuous mobile vision. DeepCache benefits model execution efficiency by exploiting …

Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning

M Sponner, B Waschneck, A Kumar - ACM Computing Surveys, 2024 - dl.acm.org
Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Neighbourhood representative sampling for efficient end-to-end video quality assessment

H Wu, C Chen, L Liao, J Hou, W Sun… - … on Pattern Analysis …, 2023 - ieeexplore.ieee.org
The increased resolution of real-world videos presents a dilemma between efficiency and
accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original …

Hardware acceleration of sparse and irregular tensor computations of ml models: A survey and insights

S Dave, R Baghdadi, T Nowatzki… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Non-structured DNN weight pruning—Is it beneficial in any platform?

X Ma, S Lin, S Ye, Z He, L Zhang… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

A data aggregation approach exploiting spatial and temporal correlation among sensor data in wireless sensor networks

L Dash, BK Pattanayak, SK Mishra, KS Sahoo… - Electronics, 2022 - mdpi.com
Wireless sensor networks (WSNs) have various applications which include zone
surveillance, environmental monitoring, event tracking where the operation mode is long …