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

• URL: http://arxiv.org/abs/2505.10607v1
• Date: Thu, 15 May 2025 16:35:33 GMT
• Title: MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
• Authors: Patara Trirat, Jae-Gil Lee,
• Abstract summary: We propose MONAQ, a novel framework that reformulates NAS into Multi-Objective Neural Architecture Querying tasks.<n>MonAQ is equipped with multimodal query generation for processing multimodal time-series inputs and hardware constraints.<n> Experiments on fifteen datasets demonstrate that MONAQ-discovered models outperform both handcrafted models and NAS baselines.
• Score: 16.639965422376303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset [39.309940166755396]
  Time-series data are critical in diverse applications, such as industrial monitoring, medical diagnostics, and climate research.<n>We introduce the Time-Series Question Answering (Time-Series QA) task and release EngineMT-QA.<n>We propose the Instruct Time Transformer (ITFormer), a novel framework that bridges time-series encoders with frozen large language models.
  arXiv  Detail & Related papers  (2025-06-25T02:33:47Z)
• Optimizing edge AI models on HPC systems with the edge in the loop [0.0]
  AI and machine learning models deployed on edge devices are often small in size.<n>It is possible to leverage hardware-aware Neural Architecture Search (NAS), an approach that seeks to systematically explore the architecture space to find optimized configurations.<n>In this study, a hardware-aware NAS workflow is introduced that couples an edge device located in Belgium with a powerful High-Performance Computing system in Germany.<n>The approach is verified on a use case in the AM domain, based on the open RAISE-LPBF dataset, achieving 8.8 times faster inference speed while simultaneously enhancing model quality by a factor of 1.35
  arXiv  Detail & Related papers  (2025-05-26T13:47:36Z)
• TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents [17.296425855109426]
  We introduce TimeSeriesGym, a scalable benchmarking framework for evaluating Artificial Intelligence (AI) agents.<n>TimeSeriesGym incorporates challenges from diverse sources spanning multiple domains and tasks.<n>We implement evaluation mechanisms for multiple research artifacts, including submission files, code, and models.
  arXiv  Detail & Related papers  (2025-05-19T16:11:23Z)
• LEMUR Neural Network Dataset: Towards Seamless AutoML [34.04248949660201]
  We introduce LEMUR, an open source dataset of neural network models with well-structured code for diverse architectures.<n>LEMUR is primarily designed to enable fine-tuning of large language models for automated machine learning tasks.<n>LEMUR will be released as an open source project under the MIT license upon acceptance of the paper.
  arXiv  Detail & Related papers  (2025-04-14T09:08:00Z)
• MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism [67.56918651825056]
  We propose a new decoder architecture with the parallel Multi-time Inquiries (MI) mechanism.<n>Our MI based model, MI-DETR, outperforms all existing DETR-like models on COCO benchmark.<n>A series of diagnostic and visualization experiments demonstrate the effectiveness, rationality, and interpretability of MI.
  arXiv  Detail & Related papers  (2025-03-03T12:19:06Z)
• Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement [55.2439260314328]
  Time Series Multi-Task Question Answering (Time-MQA) is a unified framework that enables natural language queries across multiple time series tasks.<n>Central to Time-MQA is the TSQA dataset, a large-scale dataset containing $sim $200k question-answer pairs.
  arXiv  Detail & Related papers  (2025-02-26T13:47:13Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator [4.09225917049674]
  Transferable NAS has emerged, generalizing the search process from dataset-dependent to task-dependent. This paper introduces POMONAG, extending DiffusionNAG via a many-optimal diffusion process. Results were validated on two search spaces -- NAS201 and MobileNetV3 -- and evaluated across 15 image classification datasets.
  arXiv  Detail & Related papers  (2024-09-30T16:05:29Z)
• POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification [8.190723030003804]
  This article presents the third version of a sequential model-based NAS algorithm targeting different hardware environments and multiple classification tasks. Our method is able to find competitive architectures within large search spaces, while keeping a flexible structure and data processing pipeline to adapt to different tasks. The experiments performed on images and time series classification datasets provide evidence that POPNASv3 can explore a large set of assorted operators and converge to optimal architectures suited for the type of data provided under different scenarios.
  arXiv  Detail & Related papers  (2022-12-13T17:14:14Z)
• Merlion: A Machine Learning Library for Time Series [73.46386700728577]
  Merlion is an open-source machine learning library for time series. It features a unified interface for models and datasets for anomaly detection and forecasting. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.
  arXiv  Detail & Related papers  (2021-09-20T02:03:43Z)
• ISyNet: Convolutional Neural Networks design for AI accelerator [0.0]
  Current state-of-the-art architectures are found with neural architecture search (NAS) taking model complexity into account. We propose a measure of hardware efficiency of neural architecture search space - matrix efficiency measure (MEM); a search space comprising of hardware-efficient operations; a latency-aware scaling method. We show the advantage of the designed architectures for the NPU devices on ImageNet and the generalization ability for the downstream classification and detection tasks.
  arXiv  Detail & Related papers  (2021-09-04T20:57:05Z)
• Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search [0.0]
  We introduce two generalizability strategies which include fine-tuning using a base model trained on a specific hardware and NAS search space. We provide a family of latency prediction models that achieve over 50% lower RMSE loss as compared to ProxylessNAS.
  arXiv  Detail & Related papers  (2021-01-04T00:48:09Z)
• MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
  We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS) We employ a one-shot architecture search approach in order to obtain a reduced search cost. We achieve state-of-the-art results in terms of accuracy-speed trade-off.
  arXiv  Detail & Related papers  (2020-09-29T11:56:01Z)
• NAS-Count: Counting-by-Density with Neural Architecture Search [74.92941571724525]
  We automate the design of counting models with Neural Architecture Search (NAS) We introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet)
  arXiv  Detail & Related papers  (2020-02-29T09:18:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.