On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

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 …

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 …

To mitigate the high inference latency stemming from autoregressive decoding in Large
Language Models (LLMs), Speculative Decoding has emerged as a novel decoding …

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

Transformers have recently revolutionized the machine learning (ML) landscape, gradually
making their way into everyday tasks and equipping our computers with" sparks of …

Large models based on the Transformer architecture play increasingly vital roles in artificial
intelligence, particularly within the realms of natural language processing (NLP) and …

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

Many billion-scale large language models (LLMs) have been released for resource-
constraint mobile devices to provide local LLM inference service when cloud-based …

On-device Large Language Models (LLMs) are revolutionizing mobile AI, enabling
applications such as UI automation while addressing privacy concerns. Currently, the …