Large pretrained models, coupled with fine-tuning, are slowly becoming established as the
dominant architecture in machine learning. Even though these models offer impressive …

Large pretrained models, coupled with fine-tuning, are slowly becoming established as the
dominant architecture in machine learning. Even though these models offer impressive …

Deep neural networks (DNNs) have been successfully applied in various fields. In DNNs, a
large number of multiply-accumulate (MAC) operations is required to be performed, posing …

Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has
been widely studied recently. A typical EDNN has multiple prediction heads at different …

DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

Modern deep neural networks are powerful and widely applicable models that extract task-
relevant information through multi-level abstraction. Their cross-domain success, however …

Budgeted adaptive inference with early exits is an emerging technique to improve the
computational efficiency of deep neural networks (DNNs) for edge AI applications with …

Deep Neural Networks (DNNs) are generally designed as sequentially cascaded
differentiable blocks/layers with a prediction module connected only to its last layer. DNNs …

Recently, dynamic inference has emerged as a promising way to reduce the computational
cost of deep convolutional neural networks (CNNs). In contrast to static methods (eg, weight …

Early exits are an important efficiency mechanism integrated into deep neural networks that
allows for the termination of the network's forward pass before processing through all its …