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

Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

The use of Artificial Intelligence (ai), and more specifically of Deep Learning (dl), in modern
software systems, is nowadays widespread and continues to grow. At the same time, its …

Pre-trained Transformer-based models have achieved state-of-the-art performance for
various Natural Language Processing (NLP) tasks. However, these models often have …

Vision Transformers (ViTs) have achieved impressive performance over various computer
vision tasks. However, modeling global correlations with multi-head self-attention (MSA) …

CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …

State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be
accommodated on resource constrained embedded devices. Moreover, with the …

Abstract Large Language Models (LLMs) have achieved great success in solving difficult
tasks across many domains, but such success comes with a high computation cost, and …

In this paper, we develop a novel Joint Model Compression (referred to as JMC) method by
combining structured pruning and dense knowledge distillation techniques to significantly …