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 …
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 …
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 …
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 …
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 …
An adaptive DNN inference acceleration framework with end–edge–cloud collaborative computing
Abstract Deep Neural Networks (DNNs) based on intelligent applications have been
intensively deployed on mobile devices. Unfortunately, resource-constrained mobile devices …
intensively deployed on mobile devices. Unfortunately, resource-constrained mobile devices …
Ccied: Cache-aided collaborative intelligence between edge devices
C Hu, Y Bai, R Wang, C Liu… - 2020 IEEE 22nd …, 2020 - ieeexplore.ieee.org
Recently, deep learning technology has shined in the fields of computer vision, natural
language processing and speech recognition, and related products have sprung up like …
language processing and speech recognition, and related products have sprung up like …
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 …
Delay-aware DNN inference throughput maximization in edge computing via jointly exploring partitioning and parallelism
Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to
overwhelming explosions of mobile applications, by offloading the compute-intensive tasks …
overwhelming explosions of mobile applications, by offloading the compute-intensive tasks …
FGPA: Fine-grained pipelined acceleration for depthwise separable CNN in resource constraint scenarios
C Xiao, D Xu, S Qiu, C Shi… - … IEEE Intl Conf on Parallel & …, 2021 - ieeexplore.ieee.org
Depthwise Separable Convolution can effectively reduce parameters and operations with
little loss in precision, which becomes more and more popular in many innovative neural …
little loss in precision, which becomes more and more popular in many innovative neural …