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 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 …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
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 …
Dynamic graph convolutional networks
In many different classification tasks it is required to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the …
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the …
AI on the edge: a comprehensive review
W Su, L Li, F Liu, M He, X Liang - Artificial Intelligence Review, 2022 - Springer
With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …
on data centers and network bandwidth. To ease the pressure on data centers, edge …
Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments
Recently, the application of Artificial Intelligence (AI) in the Internet of Things (IoT) devices is
increasing. As these devices are limited in processing and storing massive computations of …
increasing. As these devices are limited in processing and storing massive computations of …
On-device learning systems for edge intelligence: A software and hardware synergy perspective
Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …
exploit the computational power of clusters. However, this in-cloud computing scheme …
Compacting deep neural networks for Internet of Things: Methods and applications
Deep neural networks (DNNs) have shown great success in completing complex tasks.
However, DNNs inevitably bring high computational cost and storage consumption due to …
However, DNNs inevitably bring high computational cost and storage consumption due to …
A comprehensive survey on training acceleration for large machine learning models in IoT
The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …
many areas, eg, smart home, computer vision, natural language processing, etc. Behind …