Recently, deploying deep neural network (DNN) models via collaborative inference, which
splits a pre-trained model into two parts and executes them on user equipment (UE) and …

Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning
applications, and have been widely used on mobile devices. Running DNNs on …

Mobile devices are becoming increasingly capable of delivering intelligent services by
leveraging deep learning architectures such as deep neural networks (DNNs). However …

Today's intelligent applications can achieve high performance accuracy using machine
learning (ML) techniques, such as deep neural networks (DNNs). Traditionally, in a remote …

The success of deep neural networks (DNNs) as an enabler of artificial intelligence (AI) is
heavily dependent on high computational resources. The increasing demands for …

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 …

Distributing the inference of convolutional neural network (CNN) to multiple mobile devices
has been studied in recent years to achieve real-time inference without losing accuracy …

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 …

Deep Neural Networks (DNNs) have been widely deployed in mobile applications. DNN
inference latency is a critical metric to measure the service quality of those applications …

The success of deep neural networks (DNNs) is heavily dependent on computational
resources. While DNNs are often employed on cloud servers, there is a growing need to …