Distributed artificial intelligence empowered by end-edge-cloud computing: A survey
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 …
also supports artificial intelligence evolving from a centralized manner to a distributed one …
Scalefl: Resource-adaptive federated learning with heterogeneous clients
Federated learning (FL) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …
continuous learning and client privacy by default. In most FL approaches, all edge clients …
Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning
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 …
circumstances at runtime to improve the resource footprint while maintaining the model's …
A comprehensive benchmark of deep learning libraries on mobile devices
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …
To support fast inference of on-device DL, DL libraries play a critical role as algorithms and …
Smart at what cost? characterising mobile deep neural networks in the wild
With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is
gaining traction as devices become more powerful. With applications ranging from visual …
gaining traction as devices become more powerful. With applications ranging from visual …
Adadet: An adaptive object detection system based on early-exit neural networks
Recent researchers have proposed adaptive inference methods with an early-exiting
mechanism, which stops the inference procedure of input if the prediction is with high …
mechanism, which stops the inference procedure of input if the prediction is with high …
Path independent equilibrium models can better exploit test-time computation
Designing networks capable of attaining better performance with an increased inference
budget is important to facilitate generalization to harder problem instances. Recent efforts …
budget is important to facilitate generalization to harder problem instances. Recent efforts …
Rethinking mobile AI ecosystem in the LLM era
J Yuan, C Yang, D Cai, S Wang, X Yuan… - arXiv preprint arXiv …, 2023 - arxiv.org
In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep
learning models aimed at local execution. A key realization driving this work is the notable …
learning models aimed at local execution. A key realization driving this work is the notable …
Green edge AI: A contemporary survey
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …
Architecting efficient multi-modal aiot systems
Multi-modal computing (M 2 C) has recently exhibited impressive accuracy improvements in
numerous autonomous artificial intelligence of things (AIoT) systems. However, this …
numerous autonomous artificial intelligence of things (AIoT) systems. However, this …