Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …
Things (IoT) applications and services, spanning from recommendation systems and speech …
Self-aware distributed deep learning framework for heterogeneous IoT edge devices
Implementing artificial intelligence (AI) in the Internet of Things (IoT) involves a move from
the cloud to the heterogeneous and low-power edge, following an urgent demand for …
the cloud to the heterogeneous and low-power edge, following an urgent demand for …
[HTML][HTML] A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities
The Internet of Things (IoT) is transforming various domains, including smart energy
management, by enabling the integration of complex digital and physical components in …
management, by enabling the integration of complex digital and physical components in …
Edge-based collaborative training system for artificial intelligence-of-things
The descending of intelligence from the cloud to the heterogeneous and low-power edge in
the Artificial Intelligence-of-Things prevents uploading user-sensitive information to the …
the Artificial Intelligence-of-Things prevents uploading user-sensitive information to the …
A hybrid fast inference approach with distributed neural networks for edge computing enabled UAV swarm
Nowadays, unmanned aerial vehicle (UAV) swarm supported by mobile edge computing is
attracting more and more attention, such as smart agriculture, smart transportation, smart …
attracting more and more attention, such as smart agriculture, smart transportation, smart …
Base-reconfigurable segmented logarithmic quantization and hardware design for deep neural networks
The growth in the size of deep neural network (DNN) models poses both computational and
memory challenges to the efficient and effective implementation of DNNs on platforms with …
memory challenges to the efficient and effective implementation of DNNs on platforms with …
DIAPASON: Differentiable Allocation, Partitioning and Fusion of Neural Networks for Distributed Inference
Concerns in areas such as privacy, energy consumption, climate gas emissions, and costs,
push the trend of migrating neural network inference from being executed on the cloud to …
push the trend of migrating neural network inference from being executed on the cloud to …