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

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Pruning the parameters of deep neural networks has generated intense interest due to
potential savings in time, memory and energy both during training and at test time. Recent …

Works on lottery ticket hypothesis (LTH) and single-shot network pruning (SNIP) have raised
a lot of attention currently on post-training pruning (iterative magnitude pruning), and before …

Large pre-trained transformers are $\textit {show-stealer} $ in modern-day deep learning,
and it becomes crucial to comprehend the parsimonious patterns that exist within them as …

In this paper, we introduce a new perspective on training deep neural networks capable of
state-of-the-art performance without the need for the expensive over-parameterization by …

Structural pruning can simplify network architecture and improve inference speed. We
propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a …

In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural
Networks (DNNs) that addresses the challenge of optimizing the output distortion …

Pruning enables appealing reductions in network memory footprint and time complexity.
Conventional post-training pruning techniques lean towards efficient inference while …

Pruning, the task of sparsifying deep neural networks, received increasing attention recently.
Although state-of-the-art pruning methods extract highly sparse models, they neglect two …