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 …

Offloading techniques are considered one of the key enablers of deep neural network (DNN)-
based artificial intelligence (AI) services on end devices with limited computing resources …

Model partitioning is a promising technique for improving the efficiency of distributed
inference by executing partial deep neural network (DNN) models on edge servers (ESs) or …

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

In order to effectively apply BranchyNet, a DNN with multiple early-exit branches, in edge
intelligent applications, one way is to divide and distribute the inference task of a …

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

The proliferation of extensive neural network architectures, particularly deep learning
models, presents a challenge in terms of resource-intensive training. GPU memory …

Advancements in AI are transforming the automotive industry, creating opportunities for AI-
powered software and hardware. AI-driven features in automobiles are increasingly …

With the growing demand for real-time intelligence driven by device-to-device (D2D)
communication, deploying Deep Learning (DL) applications at the network edge becomes …

Artificial Intelligence (AI) applications are gaining popularity as they seamlessly integrate
into end-user devices, enhancing the quality of life. In recent years, there has been a …