Large language models (LLMs) have shown remarkable capabilities in language
understanding and generation. However, such impressive capability typically comes with a …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

Efficient deployment of transformer models in practice is challenging due to their inference
cost including memory footprint, latency, and power consumption, which scales quadratically …

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

Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and
long inference delay prevent Transformer-based pretrained language models (PLMs) from …

Pre-trained code representation models such as CodeBERT have demonstrated superior
performance in a variety of software engineering tasks, yet they are often heavy in …

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous
advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem …

Despite transformers' impressive accuracy, their computational cost is often prohibitive to
use with limited computational resources. Most previous approaches to improve inference …

Large foundation models, including large language models, vision transformers, diffusion,
and LLM-based multimodal models, are revolutionizing the entire machine learning …

Transformer architecture has become the de-facto model for many machine learning tasks
from natural language processing and computer vision. As such, improving its computational …