TreeNet based fast task decomposition for resource-constrained edge intelligence
Edge intelligence is an emerging technology that integrates edge computing and deep
learning to bring AI to the network's edge. It has gained wide attention for its lower network …
learning to bring AI to the network's edge. It has gained wide attention for its lower network …
Cooperative distributed deep neural network deployment with edge computing
Deep Neural Networks (DNNs) are widely used to analyze the abundance of data collected
by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data …
by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data …
Scission: Performance-driven and context-aware cloud-edge distribution of deep neural networks
L Lockhart, P Harvey, P Imai, P Willis… - 2020 IEEE/ACM 13th …, 2020 - ieeexplore.ieee.org
Partitioning and distributing deep neural networks (DNNs) across end-devices, edge
resources and the cloud has a potential twofold advantage: preserving privacy of the input …
resources and the cloud has a potential twofold advantage: preserving privacy of the input …
GEQ: Gaussian kernel inspired equilibrium models
Despite the connection established by optimization-induced deep equilibrium models
(OptEqs) between their output and the underlying hidden optimization problems, the …
(OptEqs) between their output and the underlying hidden optimization problems, the …
Collective deep reinforcement learning for intelligence sharing in the internet of intelligence-empowered edge computing
Edge intelligence is emerging as a new interdiscipline to push learning intelligence from
remote centers to the edge of the network. However, with its widespread deployment, new …
remote centers to the edge of the network. However, with its widespread deployment, new …
Edge computing and networking resource management for decomposable deep learning: An auction-based approach
With the rapid growth in the demand for internet-of-things (IoT) systems such as factory of
future, smart home, smart city, long-term healthcare, deep learning (DL) applications have …
future, smart home, smart city, long-term healthcare, deep learning (DL) applications have …
Adaptive Deep Neural Network Ensemble for Inference-as-a-Service on Edge Computing Platforms
The momentous enabling of deep learning (DL)-powered mobile application is posing a
soaring demand for computing resources that can hardly be satisfied by mobile devices. In …
soaring demand for computing resources that can hardly be satisfied by mobile devices. In …
Federated learning-based computation offloading optimization in edge computing-supported internet of things
Recent visualizations of smart cities, factories, healthcare system and etc. raise challenges
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …
Fine-grained elastic partitioning for distributed dnn towards mobile web ar services in the 5g era
Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has
attracted considerable attention in mobile Web AR and other services. However, neither …
attracted considerable attention in mobile Web AR and other services. However, neither …
Edgepipe: Tailoring pipeline parallelism with deep neural networks for volatile wireless edge devices
As intelligence recently moves to the edge to tackle the problems of privacy, scalability, and
network bandwidth in the centralized intelligence, it is necessary to construct an efficient yet …
network bandwidth in the centralized intelligence, it is necessary to construct an efficient yet …