Abstract
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.
Abstract (translated)
智能手机和物联网设备的广泛使用要求在资源受限的硬件上进行高效的时间序列分析，这对于诸如人类活动识别和空气质量预测等传感应用至关重要。最近关于硬件感知神经架构搜索（NAS）的努力已经实现了针对特定平台的自动化架构发现，然而没有一种方法专注于边缘部署的一般时间序列分析。通过利用大型语言模型（LLM）的问题解决和推理能力，我们提出了MONAQ这一新框架，它将NAS重新定义为多目标神经架构查询任务。MONAQ配备了多模态查询生成功能，用于处理多模态时间序列输入和硬件约束，并且采用基于LLM代理的多目标搜索来通过代码生成实现可以直接部署的模型。通过整合数值数据、时间序列图像和文本描述，MONAQ提升了LLM对时间序列数据的理解能力。在十五个数据集上的实验表明，与手工设计的模型和NAS基线相比，MONAQ发现的模型不仅性能更优，而且更加高效。
URL
https://arxiv.org/abs/2505.10607
