Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in
the current “smart everything” scenario, this article provides a comprehensive overview of …

With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as
it does not require retraining and entails no loss in accuracy. Unfortunately, resource …

Collaborative inference is a promising method for balancing the limited computational power
of Internet of Things (IoT) devices with the huge computational demands of convolutional …

With significant potential improvement in device-to-device (D2D) communication due to
improved wireless link capacity (eg, 5G and NextG systems), a collaboration of multiple …

Embedded distributed inference of Neural Networks has emerged as a promising approach
for deploying machine-learning models on resource-constrained devices in an efficient and …

Deep neural network (DNN) partition is a research problem that involves splitting a DNN into
multiple parts and offloading them to specific locations. Because of the recent advancement …

Deep learning (DL) has gained increasing prominence in latency-critical artificial
intelligence (AI) applications. Due to the intensive computational requirements of these …

The advent of Deep Neural Networks (DNNs) has empowered numerous computer-vision
applications. Due to the high computational intensity of DNN models, as well as the resource …

Abstract Deep Neural Networks (DNNs) have shown exceptional promise in providing
Artificial Intelligence (AI) to many computer vision applications. Nevertheless, complex …