用大模型自动设计适合手机的时序分析模型，兼顾准确与省电。
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
- 把模型搜索变成多目标提问任务，用大模型生成适配设备的代码。
- 在15个数据集上比手工设计和传统方法更准更省资源。
- 支持多种输入形式，适合做穿戴设备或空气质量监测的开发者。
智能手机和物联网设备的普及要求在资源受限硬件上实现高效的时序分析，这对人体活动识别和空气质量预测等传感应用至关重要。现有硬件感知神经架构搜索（NAS）虽能自动化设计特定平台的模型，但未针对边缘部署的通用时序分析任务。本文提出MONAQ框架，将NAS重构为多目标神经架构查询任务，利用大语言模型（LLM）的问题求解与推理能力。MONAQ采用多模态查询生成机制，处理多模态时序输入与硬件约束，并通过基于LLM代理的多目标搜索，实现可直接部署的模型代码生成。通过融合数值数据、时序图像与文本描述，提升LLM对时序数据的理解能力。在15个数据集上的实验表明，MONAQ发现的模型在性能与效率上均优于手工设计模型及现有NAS基线。
原文摘要 · Abstract (English)
The growing use of smartphones and IoT devices necessitates efficient time-series analysis on resource-constrained hardware, which is critical for sensing applications such as human activity recognition and air quality prediction. Recent efforts in hardware-aware neural architecture search (NAS) automate architecture discovery for specific platforms; however, none focus on general time-series analysis with edge deployment. Leveraging the problem-solving and reasoning capabilities of large language models (LLM), we propose MONAQ, a novel framework that reformulates NAS into Multi-Objective Neural Architecture Querying tasks. MONAQ is equipped with multimodal query generation for processing multimodal time-series inputs and hardware constraints, alongside an LLM agent-based multi-objective search to achieve deployment-ready models via code generation. By integrating numerical data, time-series images, and textual descriptions, MONAQ improves an LLM's understanding of time-series data. Experiments on fifteen datasets demonstrate that MONAQ-discovered models outperform both handcrafted models and NAS baselines while being more efficient.
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