Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Large language models (LLMs) are crucial in modern natural language processing and
artificial intelligence. However, they face challenges in managing their significant memory …

The increasing demand for improving deep learning model performance has led to a
paradigm shift in supporting low-precision computation to harness the robustness of deep …

Large language models (LLMs) have achieved remarkable advancements in natural
language processing, showcasing exceptional performance across various tasks. However …

Vision Transformers (ViTs) have recently garnered considerable attention, emerging as a
promising alternative to convolutional neural networks (CNNs) in several vision-related …

Recent advancements in large language models (LLMs) are propelling us toward artificial
general intelligence with their remarkable emergent abilities and reasoning capabilities …

Matrix multiplication (MatMul) typically dominates the overall computational cost of large
language models (LLMs). This cost only grows as LLMs scale to larger embedding …

In spite of the outstanding performance, Neural Architecture Search (NAS) is criticized for
massive computation. Recently, Zero-shot NAS has emerged as a promising approach by …

In this paper, we present the first structural binarization method for LLM compression to less
than 1-bit precision. Although LLMs have achieved remarkable performance, their memory …

As large language model (LLM) inference demands ever-greater resources, there is a rapid
growing trend of using low-bit weights to shrink memory usage and boost inference …