Recently, the notions of partitioning the Deep Neural Network (DNN) model over the multi-
level computing units and making a fast inference with the early-inference technique have …

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 …

This paper studies the problem of predicting the deep learning (DL) jobs' training time which
is the fundamental guide for training resources allocating and job scheduling. The existing …

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 …

It is a dilemma to balance the tradeoff between the computation efficiency and
communication cost of deploying deep learning models in the mobile edge computing …

With the development of smart devices, the computing capabilities of portable end devices
such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud …

Mobile devices can generate a tremendous amount of unique data, and thus, create
countless opportunities for deep learning tasks. Due to the concerns of data privacy, it is …

This paper investigates the problem of performing distributed deep learning (DDL) to train
machine learning (ML) models at the edge with resource-constrained embedded devices …

We address distributed machine learning in multitier (eg, mobile-edge-cloud) networks
where a heterogeneous set of nodes cooperate to perform a learning task. Due to the …

Convolutional neural networks (CNNs) have shown remarkable performance in various
computer vision tasks in recent years. However, the increasing model size has raised …