Abstract Deep Neural Networks (DNNs) have proven effective in various applications due to
their dominant performance. However, integrating DNNs into edge devices remains …

Abstract Deep Neural Networks (DNNs) have gained widespread popularity in different
domain applications due to their dominant performance. Despite the prevalence of …

Abstract Deep Neural Networks (DNNs) have gained widespread popularity in different
domain applications due to their dominant performance. Despite the prevalence of …

Deep neural networks (DNNs) have recently gained widespread application in various
domains. However, the computational and memory requirements of DNN models pose …

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using
a single edge device due to the limited resources. To facilitate intelligent applications across …

Deep Neural Networks (DNNs) are becoming extremely popular for many modern
applications deployed at the edge of the computing continuum. Despite their effectiveness …

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 …

The computation and storage requirements of Deep Neural Networks (DNNs) make them
challenging to deploy on edge devices, which often have limited resources. Conversely …

Due to the high computational demands inherent in Deep Neural Network (DNN)
executions, multi-tier environments have emerged as preferred platforms for DNN inference …

Training a deep neural network (DNN) requires substantial computational and memory
requirements. It is common to use multiple devices to train a DNN to reduce the overall …