Deep learning in human activity recognition with wearable sensors: A review on advances
Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …
tracking, wellness monitoring, and human–computer interaction, that measure and improve …
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 …
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 …
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 …
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 …
UNPU: An energy-efficient deep neural network accelerator with fully variable weight bit precision
An energy-efficient deep neural network (DNN) accelerator, unified neural processing unit
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …
(UNPU), is proposed for mobile deep learning applications. The UNPU can support both …
Google workloads for consumer devices: Mitigating data movement bottlenecks
We are experiencing an explosive growth in the number of consumer devices, including
smartphones, tablets, web-based computers such as Chromebooks, and wearable devices …
smartphones, tablets, web-based computers such as Chromebooks, and wearable devices …
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 …
Transforming large-size to lightweight deep neural networks for IoT applications
Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …
order performance and automated feature extraction capability. This has encouraged …
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 …