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

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

The effectiveness of deep neural networks (DNN) in vision, speech, and language
processing has prompted a tremendous demand for energy-efficient high-performance DNN …

Budgeted pruning is the problem of pruning under resource constraints. In budgeted
pruning, how to distribute the resources across layers (ie, sparsity allocation) is the key …

Deep model compression has been extensively studied, and state-of-the-art methods can
now achieve high compression ratios with minimal accuracy loss. This paper studies model …

Abstract Model compression techniques are recently gaining explosive attention for
obtaining efficient AI models for various real time applications. Channel pruning is one …

Leveraging sparsity in deep neural network (DNN) models is promising for accelerating
model inference. Yet existing GPUs can only leverage the sparsity from weights but not …

Network pruning can reduce the high computation cost of deep neural network (DNN)
models. However, to maintain their accuracies, sparse models often carry randomly …

Dynamic model pruning is a recent direction that allows for the inference of a different sub-
network for each input sample during deployment. However, current dynamic methods rely …

Generative adversarial networks (GANs) have gained increasing popularity in various
computer vision applications, and recently start to be deployed to resource-constrained …