Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Connected and autonomous vehicles is a disruptive technology that has the potential to
transform the current transportation system by reducing traffic accidents and enhancing …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Recent advancements in deep neural networks (DNN) enabled various mobile deep
learning applications. However, it is technically challenging to locally train a DNN model due …

In this paper, we analyze two popular network compression techniques, ie filter pruning and
low-rank decomposition, in a unified sense. By simply changing the way the sparsity …

Structured pruning is a commonly used technique in deploying deep neural networks
(DNNs) onto resource-constrained devices. However, the existing pruning methods are …

This paper presents a survey of methods for pruning deep neural networks. It begins by
categorising over 150 studies based on the underlying approach used and then focuses on …

Network pruning has been the driving force for the acceleration of neural networks and the
alleviation of model storage/transmission burden. With the advent of AutoML and neural …

In instance-level detection tasks (eg, object detection), reducing input resolution is an easy
option to improve runtime efficiency. However, this option severely hurts the detection …