The last decade has seen the emergence of Deep Neural Networks (DNNs) as the de facto
algorithm for various computer vision applications. In intelligent edge devices, sensor data …

Edge computing has emerged as a promising line of research for processing large-scale
data and providing low-latency services. Unfortunately, deploying deep neural networks …

In edge computing, most procedures, including data collection, data processing, and service
provision, are handled at edge nodes and not in the central cloud. This decreases the …

Abstract Deep Neural Networks (DNNs) based on intelligent applications have been
intensively deployed on mobile devices. Unfortunately, resource-constrained mobile devices …

Deep Neural Networks (DNN) have been widely used in a large number of application
scenarios. However, DNN models are generally both computation-intensive and memory …

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by
model partitioning, which is particularly useful for intelligent scenarios that demand high …

Reducing inference time and energy usage while maintaining prediction accuracy has
become a significant concern for deep neural networks (DNN) inference on resource …

For model inference of convolutional neural networks (CNNs), we nowadays witness a shift
from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute-and …

Computing at the edge is increasingly important since a massive amount of data is
generated. This poses challenges in transporting all that data to the remote data centers and …

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge
resources and the cloud has a potential twofold advantage: preserving privacy of the input …