Joint optimization of data transfer and co-execution for DNN in edge computing
Deep learning plays an increasingly important role in human life. However, resource-
constrained IoT devices are still inefficient in performing deep neural network (DNN) …
constrained IoT devices are still inefficient in performing deep neural network (DNN) …
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 …
Accelerate cooperative deep inference via layer-wise processing schedule optimization
Computation offloading is proposed to solve one obstacle of enabling high-accurate and
real-time deep inference in resource-constrained Internet of Things (IoT) devices …
real-time deep inference in resource-constrained Internet of Things (IoT) devices …
DNN inference acceleration with partitioning and early exiting in edge computing
Recently, deep neural networks (DNNs) have been applied to most intelligent applications
and deployed on different kinds of devices. However, DNN inference is resource-intensive …
and deployed on different kinds of devices. However, DNN inference is resource-intensive …
EEAI: An End-edge Architecture for Accelerating Deep Neural Network Inference
Deep Neural Networks (DNNs), as a key technology for Artificial Intelligence (AI)
applications in the 5G era, have been widely used in the field of mobile intelligence …
applications in the 5G era, have been widely used in the field of mobile intelligence …
Enable pipeline processing of DNN co-inference tasks in the mobile-edge cloud
Deep Neural Network (DNN) based artificial intelligence help driving the great development
of mobile Internet. However, the hardware of a mobile device may not be sufficiently to meet …
of mobile Internet. However, the hardware of a mobile device may not be sufficiently to meet …
Edge intelligence: On-demand deep learning model co-inference with device-edge synergy
As the backbone technology of machine learning, deep neural networks (DNNs) have have
quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices …
quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices …
Enabling low latency edge intelligence based on multi-exit dnns in the wild
In recent years, deep neural networks (DNNs) have witnessed a booming of artificial
intelligence Internet of Things applications with stringent demands across high accuracy and …
intelligence Internet of Things applications with stringent demands across high accuracy and …
Toward decentralized and collaborative deep learning inference for intelligent IoT devices
Deep learning technologies are empowering IoT devices with an increasing number of
intelligent services. However, the contradiction between resource-constrained IoT devices …
intelligent services. However, the contradiction between resource-constrained IoT devices …
Edge AI: On-demand accelerating deep neural network inference via edge computing
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep
Neural Networks (DNNs) have quickly attracted widespread attention. However, it is …
Neural Networks (DNNs) have quickly attracted widespread attention. However, it is …