The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

Large language models (LLMs) have the potential to revolutionize behavioral science by
accelerating and improving the research cycle, from conceptualization to data analysis …

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

Being more powerful and intrusive into user-device interactions, LLMs are eager for on-
device execution to better preserve user privacy. In this work, we propose a new paradigm of …

Recurrent neural networks (RNNs) notoriously struggle to learn long-term memories,
primarily due to vanishing and exploding gradients. The recent success of state-space …

The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

Regulation is nothing without enforcement. This particularly holds for the dynamic field of
emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's …

Low precision training and inference affect both the quality and cost of language models, but
current scaling laws do not account for this. In this work, we devise" precision-aware" scaling …

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