We propose a communication-efficient collaborative inference framework in the domain of
edge inference, focusing on the efficient use of vision transformer (ViTs) models. The …

Recent years have witnessed the great success of vision transformer (ViT), which has
achieved state-of-the-art performance on multiple computer vision benchmarks. However …

Edge Intelligence has received attention in the recent times for its potential towards
improving responsiveness, reducing the cost of data transmission, enhancing security and …

Many applications require real-time processing and response times that arenot achievable
with cloud-based processing. Towards this end, Edge intelligence improves …

Deep neural networks are increasingly used in end devices such as mobile phones to
support novel features, eg, image classification. Traditional paradigms to support mobile …

Distributed edge inference emerges to be a promising paradigm to speed up inference.
Previous works make physical partitions on CNNs to realize it, but there are the following …

Ensemble-based collaborative inference systems, Edge Ensembles, are deep learning edge
inference systems that enhance accuracy by aggregating predictions from models deployed …

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

In edge-cloud collaborative intelligence (CI), an unreliable transmission channel exists in
the information path of the AI model performing the inference. It is important to be able to …

Collaborative perception empowers each agent to improve its perceptual ability through the
exchange of perceptual messages with other agents. It inherently results in a fundamental …