Split inference facilitates deep neural network (DNN) inference tasks at resource-
constrained edge devices. However, a pre-determined split configuration of a DNN limits the …

Deploying deep neural networks (DNNs) on IoT and mobile devices is a challenging task
due to their limited computational resources. Thus, demanding tasks are often entirely …

With the advances of machine learning, edge computing, and wireless communications, split
inference has tracked more and more attention as a versatile inference paradigm. Split …

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 …

The distributed inference framework is an emerging technology for real-time applications
empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet …

Device-edge co-inference opens up new possibilities for resource-constrained wireless
devices (WDs) to execute deep neural network (DNN)-based applications with heavy …

In the era of beyond 5G technology, it is expected that more and more applications can use
deep neural network (DNN) models for different purposes with minimum inference time …

In edge inference, an edge server provides remote-inference services to edge devices. This
requires the edge devices to upload high-dimensional features of data samples over …

Split learning is a privacy-preserving distributed learning paradigm in which an ML model
(eg, a neural network) is split into two parts (ie, an encoder and a decoder). The encoder …

Learning and inference at the edge is all about distilling, exchanging, and processing data
in a cooperative and distributed way, to achieve challenging trade-offs involving energy …