Optimizing CNN Inference Speed over Big Social Data through Efficient Model Parallelism for Sustainable Web of Things
The rapid development of artificial intelligence and networking technologies has catalyzed
the popularity of intelligent services based on deep learning in recent years, which in turn …
the popularity of intelligent services based on deep learning in recent years, which in turn …
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 …
Collaborative execution of deep neural networks on internet of things devices
With recent advancements in deep neural networks (DNNs), we are able to solve
traditionally challenging problems. Since DNNs are compute intensive, consumers, to …
traditionally challenging problems. Since DNNs are compute intensive, consumers, to …
OfpCNN: On-Demand Fine-Grained Partitioning for CNN Inference Acceleration in Heterogeneous Devices
L Yang, C Zheng, X Shen, G Xie - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Collaborative inference is a promising method for balancing the limited computational power
of Internet of Things (IoT) devices with the huge computational demands of convolutional …
of Internet of Things (IoT) devices with the huge computational demands of convolutional …
Edge Intelligence with Distributed Processing of DNNs: A Survey.
S Tang, M Cui, L Qi, X Xu - CMES-Computer Modeling in …, 2023 - search.ebscohost.com
Withthe rapiddevelopment of deep learning, the size of data sets anddeepneuralnetworks
(DNNs) models are also booming. As a result, the intolerable long time for models' training …
(DNNs) models are also booming. As a result, the intolerable long time for models' training …
On designing the adaptive computation framework of distributed deep learning models for Internet-of-Things applications
CH Tu, QH Sun, MH Cheng - The Journal of Supercomputing, 2021 - Springer
Deep learning methods have been gradually adopted in the Internet-of-Things (IoT)
applications. Nevertheless, the large demands for the computation and memory resources …
applications. Nevertheless, the large demands for the computation and memory resources …
Enabling DNN acceleration with data and model parallelization over ubiquitous end devices
Deep neural network (DNN) shows great promise in providing more intelligence to
ubiquitous end devices. However, the existing partition-offloading schemes adopt data …
ubiquitous end devices. However, the existing partition-offloading schemes adopt data …
Toward collaborative inferencing of deep neural networks on Internet-of-Things devices
Recent advancements in deep neural networks (DNNs) have enabled us to solve
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …
traditionally challenging problems. To deploy a service based on DNNs, since DNNs are …
Latency and Privacy Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems
Reliable artificial intelligence systems not only propose a challenge on providing intelligent
services with high quality for customers, but also require customers' privacy to be protected …
services with high quality for customers, but also require customers' privacy to be protected …
Aware: Adaptive Distributed Training with Computation, Communication and Position Awareness for Deep Learning Model
Y Zeng, G Yi, Y Yin, J Wu, M Xue… - 2022 IEEE 24th Int …, 2022 - ieeexplore.ieee.org
The accuracy of the neural networks can usually be improved by increasing the size of the
dataset and the layers or operators of the network, as it has strong composability. But, it …
dataset and the layers or operators of the network, as it has strong composability. But, it …