MONAQ : Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices (2025)

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 ***M***ulti-***O***bjective ***N***eural ***A***rchitecture ***Q***uerying 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.

## introduction the widespread adoption of smartphones, iot devices, and wearables has intensified the demand for efficient time-series analysis on resourceconstrained devices, essential for smart manufacturing, personalized healthcare @xcite , and so on. these devices, often based on microcontroller units (mcu), are rapidly proliferating, with over 250b units worldwide @xcite . deep learning on such affordable, energy-efficient hardware can democratize ai, enabling broad accessibility across diverse sectors. however, tiny deep learning faces unique challenges due to stringent memory constraints. typical mcus, with less than 512kb sram, and even higher-end devices like raspberry pi 4 struggle to run conventional deep neural networks @xcite . efficient ai inference demands innovative methods to navigate these limitations. moreover, designing optimal network architectures and selecting hyperparameters for such devices is timeconsuming and requires significant manual effort. hardware-aware neural architecture search (hw-nas) has emerged to automate this process, tailoring architectures to specific tasks and hardware @xcite . while nas has advanced, it predominantly focuses on computer vision tasks and lacks generalizability for time-series analysis @xcite . time-series applications span classification (e.g., human activity recognition) @xcite , regression (e.g., environmental monitoring) @xcite , and anomaly detection (e.g., industrial systems) @xcite . existing nas frameworks often target narrow use cases and fail to operate effectively within edge device constraints @xcite @xcite . furthermore, current hw-nas frameworks are frustrated by fixed search spaces and complex interfaces, making them less generalizable across tasks and inaccessible to non-experts. large language models (llm)-based nas frameworks @xcite aim to address these issues but still rely on predefined search spaces and require userprovided initial architectures, limiting flexibility and usability @xcite @xcite . in contrast, to eliminate these undesirable user burdens, we reformulate the nas problem as a multi-objective neural architecture querying (naq) problem by leveraging llms' advancements in reasoning and problem-solving. unlike existing llm-based nas frameworks (figure 1a ), which require users to define a search space or an initial set of architectures, naq only requires natural language queries from users, decoupling them user-defined search space llm data description task descriptions user feedback (experiment logs) run experiments deployable model time series (a) existing llm-based nas. task descriptions user data description time series time series images multi-agent llms deployable model monaq multimodal query generation multimodal query (b) our monaq. from the architecture search process. this approach reduces reliance on human expertise while enhancing accessibility and flexibility in the model design process. it allows users to focus on addressing the actual problems in their datasets, leaving the complex search configurations to the llm. however, achieving efficient naq poses two key challenges. by integrating these components, we present monaq, the first multi-agent llm-based naq framework with an open-ended search space for time-series analysis on resource-constrained devices. our contributions are as follows. on-device time-series analysis time-series analysis on resource-constrained devices, such as iot and wearables, has gained importance due to the need for real-time processing with limited computational and energy resources @xcite . common approaches include cnns, rnns (e.g., lstms), and transformers @xcite . while cnns excel at extracting local context, they struggle with long-term dependencies @xcite . rnns and lstms address these drawbacks but are hindered by sequential processing, increasing latency. transformers @xcite enable parallel processing and capture long-term dependencies but are often unsuitable for edge devices due to high computational demands. lightweight models like attention condensers, cnn-rnn hybrids (e.g., deepcon-vlstm (ordóñez and roggen, 2016)), and lowresource architectures like tinyhar @xcite and mlp-har @xcite balance performance with resource efficiency. hardware-aware nas (hw-nas) despite these advancements, hw-nas for time-series data remains largely underexplored. micronas @xcite introduces time-series-specific search spaces for microcontrollers, while tinyt-nas @xcite supports efficient cpu operations. however, these methods often rely on fixed search spaces, requiring significant expertise. given these challenges, there is a growing need for nas frameworks with natural language interfaces, allowing users to describe their desired architecture in plain language rather than through direct programming @xcite . leveraging llms in this context can make hw-nas more user-friendly and adaptable across a wider range of applications, leading to democratize nas processes for better accessibility and adaptability. llms for nas llms have shown potential in automating nas by leveraging pre-trained knowledge to generate diverse, high-performing architectures @xcite @xcite @xcite @xcite @xcite . however, current llm-based nas frameworks face challenges with time-series data due to their limited understanding of raw numerical inputs @xcite and their reliance on runtime feedback and user-defined search spaces, which are often timeconsuming and require intricate configurations. to address these issues, we introduce neural architecture querying, enabling users to specify requirements directly through natural language prompts for a given time-series dataset. our approach removes the need for complex configurations and simplifies the architecture search process. ## experiments to verify the effectiveness of monaq, we conduct extensive experiments on two main on-device analysis tasks: classification and regression. additionally, we perform ablation and hyperparameter studies. the source code is available at https: //github.com/kaist-dmlab/monaq . ## conclusions this paper proposes a novel nas framework, monaq, which reformulates nas problems as multi-objective neural architecture querying tasks, using multimodal time-series inputs and hardware constraints as queries to llms. to enhance the llm's understanding of time series, we introduce a multimodal query generation module and improve search efficiency via a multi-agent based search. extensive experiments on 15 datasets show that the models discovered by monaq outperform handcrafted baselines while achieving greater efficiency.