Efficient acceleration of deep learning inference on resource-constrained edge devices: A review
Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …
in breakthroughs in many areas. However, deploying these highly accurate models for data …
Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
Edge intelligence: Empowering intelligence to the edge of network
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …
caching, processing, and analysis proximity to where data are captured based on artificial …
Ai benchmark: Running deep neural networks on android smartphones
Over the last years, the computational power of mobile devices such as smartphones and
tablets has grown dramatically, reaching the level of desktop computers available not long …
tablets has grown dramatically, reaching the level of desktop computers available not long …
A first look at deep learning apps on smartphones
To bridge the knowledge gap between research and practice, we present the first empirical
study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit …
study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit …
Flexible high-resolution object detection on edge devices with tunable latency
Object detection is a fundamental building block of video analytics applications. While
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …
Edge intelligence: Architectures, challenges, and applications
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …
caching, processing, and analysis in locations close to where data is captured based on …
Tensorrt-based framework and optimization methodology for deep learning inference on jetson boards
As deep learning inference applications are increasing in embedded devices, an embedded
device tends to equip neural processing units (NPUs) in addition to a multi-core CPU and a …
device tends to equip neural processing units (NPUs) in addition to a multi-core CPU and a …
Adaptive deep learning model selection on embedded systems
The recent ground-breaking advances in deep learning networks (DNNs) make them
attractive for embedded systems. However, it can take a long time for DNNs to make an …
attractive for embedded systems. However, it can take a long time for DNNs to make an …
Deep learning on mobile and embedded devices: State-of-the-art, challenges, and future directions
Recent years have witnessed an exponential increase in the use of mobile and embedded
devices. With the great success of deep learning in many fields, there is an emerging trend …
devices. With the great success of deep learning in many fields, there is an emerging trend …