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

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated
models like OpenAI's ChatGPT, represents a significant advancement in artificial …

Large Language Models (LLMs) have emerged as powerful tools for natural language
processing tasks, revolutionizing the field with their ability to understand and generate …

Large language models (LLMs) have revolutionized natural language processing with their
exceptional capabilities. However, deploying LLMs on resource-constrained edge devices …

Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to
deploy due to their high computational and storage demands. Pruning can reduce model …

Transformers have revolutionized deep learning and generative modeling, enabling
unprecedented advancements in natural language processing tasks. However, the size of …

The attention mechanisms of transformers effectively extract pertinent information from the
input sequence. However, the quadratic complexity of self-attention incurs heavy …

Recently, transformer-based models have achieved remarkable success in various fields,
such as computer vision, speech recognition, and natural language processing. However …

Automated design of efficient transformer models has recently attracted significant attention
from industry and academia. However, most works only focus on certain metrics while …

Transformer models have emerged as the state-of-the-art in many natural language
processing and computer vision applications due to their capability of attending to longer …