A Contemporary Overview: Trends and Applications of Large Language Models on Mobile Devices
L Liu, H An, P Chen, L Ye - arXiv preprint arXiv:2412.03772, 2024 - arxiv.org
With the rapid development of large language models (LLMs), which possess powerful
natural language processing and generation capabilities, LLMs are poised to provide more …
natural language processing and generation capabilities, LLMs are poised to provide more …
EdgeLLM: Fast On-device LLM Inference with Speculative Decoding
Generative tasks, such as text generation and question answering, are essential for mobile
applications. Given their inherent privacy sensitivity, executing them on devices is …
applications. Given their inherent privacy sensitivity, executing them on devices is …
PieBridge: Fast and Parameter-Efficient On-Device Training via Proxy Networks
On-device training Neural Networks (NNs) has been a crucial catalyst towards privacy-
preserving and personalized mobile intelligence. Recently, a novel training paradigm …
preserving and personalized mobile intelligence. Recently, a novel training paradigm …
FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices
Deploying LLMs on edge devices presents serious technical challenges. Memory elasticity
is crucial for edge devices with unified memory, where memory is shared and fluctuates …
is crucial for edge devices with unified memory, where memory is shared and fluctuates …