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 …
Convergence of edge computing and deep learning: A comprehensive survey
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 …
massive amounts of data, and ever-increasing computing power is driving the core of …
Deep learning for edge computing applications: A state-of-the-art survey
With the booming development of Internet-of-Things (IoT) and communication technologies
such as 5G, our future world is envisioned as an interconnected entity where billions of …
such as 5G, our future world is envisioned as an interconnected entity where billions of …
Integrating edge intelligence and blockchain: What, why, and how
Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT),
edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a …
edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a …
hXDP: Efficient software packet processing on FPGA NICs
The network interface cards (NICs) of modern computers are changing to adapt to faster
data rates and to help with the scaling issues of general-purpose CPU technologies. Among …
data rates and to help with the scaling issues of general-purpose CPU technologies. Among …
Energy-aware inference offloading for DNN-driven applications in mobile edge clouds
With increasing focus on Artificial Intelligence (AI) applications, Deep Neural Networks
(DNNs) have been successfully used in a number of application areas. As the number of …
(DNNs) have been successfully used in a number of application areas. As the number of …
Energy or accuracy? Near-optimal user selection and aggregator placement for federated learning in MEC
To unveil the hidden value in the datasets of user equipments (UEs) while preserving user
privacy, federated learning (FL) is emerging as a promising technique to train a machine …
privacy, federated learning (FL) is emerging as a promising technique to train a machine …
Autonomous Power Management With Double-Q Reinforcement Learning Method
Energy efficiency and autonomous power management are extremely important for mobile-
edge computing. Reducing energy consumption of a number of applications running …
edge computing. Reducing energy consumption of a number of applications running …
Eciton: Very low-power lstm neural network accelerator for predictive maintenance at the edge
This paper presents Eciton, a very low-power LSTM neural network accelerator for low-
power edge sensor nodes, demonstrating real-time processing on predictive maintenance …
power edge sensor nodes, demonstrating real-time processing on predictive maintenance …
A hardware acceleration platform for AI-based inference at the edge
Abstract Machine learning (ML) algorithms are already transforming the way data are
collected and processed in the data center, where some form of AI has permeated most …
collected and processed in the data center, where some form of AI has permeated most …