Smart homes are receiving growing interest in global markets. However, current smart home assistant systems either control appliances solely through users' explicit commands or rely on cloud-based large language models (LLMs) for vague command understanding, which brings drawbacks such as high latency, privacy concern, and high cost. In this work, we propose VCU-LLM, the first system to deploy LLMs on edge devices for local vague command understanding and smart device control plan generation. VCU-LLM introduces a novel vague command knowledge retrieval algorithm that refines device-related information in the input prompt, thereby accelerating the LLM's on-device inference and reducing task complexity. We further construct a dataset for LLM fine-tuning to simulate the use of smart home assistants in controlling devices across different households. During inference, a customized KV-cache technique is applied for further inference acceleration. Our evaluations with both human-based and LLM-based scoring demonstrate that VCU-LLM improves the quality of generated control plans by an average of 43.3% compared with SOTA baselines, while reducing time overhead by an average of 8.44x compared with other on-device baselines. We also implement VCU-LLM through a case study in a real home environment, demonstrating its feasibility in real-world application.
Zhang et al. (Mon,) studied this question.