MOBISYS

Deploying large language models (LLMs) inference into mobile devices is cost-efficient for companies, and well addresses the privacy concern of users. However, the limited computation capacity and memory constraints of mobile devices hinder their ...

Privacy leakage from user queries is a widely-concerned issue in search engines and chatbot services. Existing solutions based on privacy information removal, obfuscation, and encryption may inevitably hurt service quality or require full trust of the ...

In recent years, AI has undergone significant changes. Firstly, there is a growing recognition of the need to deploy inference models based on Deep Neural Networks (DNNs) on edge devices. Secondly, there is an increasing demand for low-energy inferencing ...

Current Multi-modal LLM-based mobile agents are associated with concerns over high inference time and cost. We propose to tackle these issues by developing a lightweight UI Transition Graph (UTG) and locally executing automatic tasks. Specifically, we ...

This paper introduces ChainStream, an LLM-based framework for building and serving context-aware AI agents. Driven by the goal to enable context awareness of LLM agents and flexible information sharing between them, we adopt a stream-based design, in ...

The correlation analysis between socioeconomic factors and environmental impact is essential for policy making to ensure sustainability and economic development simultaneously. With the development of Internet of Things (IoT), citizen science IoT ...

The large language models (LLMs) that everyone is using are not deployed locally. Users need to send relatively private and important data to LLM when using it. Handing over private and important data to LLM will cause people to worry, especially now ...

Large language models (LLMs) play a crucial role in various Natural Language Processing (NLP) tasks, prompting their deployment on mobile devices for inference. However, a significant challenge arises due to high waiting latency, especially for long ...

Edge-Cloud collaboration for deep learning inference has been actively studied, to enhance the inference performance by leveraging both Edge and Cloud resources. However, traditional Edge-Cloud collaboration based on model partitioning or confidence ...