In-Depth Research and Future Development Trends of LabVIEW and Artificial Intelligence (AI)

An in-depth exploration of the potential development trajectories for LabVIEW and AI technologies.

1. Introduction

LabVIEW是由美国国家 Instruments（NI）公司开发的一款图形化系统设计平台，在测试与测量、工业自动化、控制系统以及嵌入式系统等多个领域中长期发挥着核心作用。它凭借直观的数据显示流编程范式大大简化了复杂系统的开发流程。随着人工智能技术的飞速发展，并在机器学习（ML）、深度学习（DL）、计算机视觉（CV）以及自然语言处理（NLP）等领域取得了重大突破，在将这些先进的AI能力与 LabVIEW 强大的硬件集成能力和实时控制功能相结合方面已逐渐成为提升系统智能化水平、解决具体行业难题以及探索新型商业模式的关键方向

本报告旨在致力于深入探讨LabVIEW与人工智能技术的整合研究，在涵盖具体的人工智能技术应用领域、LabVIEW的应用范围以及整合人工智能模型的方法基础上，探讨理论与实践方面的考量，并预测未来一年至两年内及五年之后的技术发展趋势。基于研究和学习的要点，请阐述LabVIEW如何拥抱人工智能所面临的挑战及其带来的机遇。

2. Current Status of LabVIEW and AI Technology Integration

Nowadays, the integration between LabVIEW and AI technologies is primarily manifest in specific areas.

2.1 Application of Core AI Technologies in LabVIEW

Computer Vision (CV): 该领域是LabVIEW最紧密集成的人工智能领域之一，并且在多个应用场景中展现出广泛的应用潜力。NI推出了Vision Development Module (VDM)，它集成了大量经典的图像处理算法，并支持部署深度学习模型。此外，一些基于图形化的第三方AI视觉开发平台也应运而生。这些平台允许用户完成物体分类、检测、测量等任务，在无需深入文本编程知识的情况下实现高效工作流程设计。它们还支持导入从TensorFlow等主流框架训练好的模型，并利用硬件加速技术（如Nvidia GPU和Intel CPU）提升推理速度和效率。

机器学习（ML）与深度学习（DL）： LabVIEW系统本身并不提供一个完整的ML/DL模型训练环境, 但通过多种方法实现了与外部ML/DL框架的集成与对接.

NI's Official Announcement: NI has made available the AI Vision Toolkit for OpenVINO for LabVIEW (AIVT-OV), built upon OpenVINO. This toolkit is designed to streamline the deployment and inference of deep neural networks in LabVIEW, with specific optimizations for Intel hardware performance. The package incorporates OpenCV functions and provides support for a range of deep learning models. Additionally, NI offers tools that integrate Python and TensorFlow, enabling LabVIEW users to effortlessly utilize these libraries for advanced analysis.

Third-Party Toolkits: 企业如Yiku智能科技则提供了LabVIEW AI视觉工具包以及Open Neural Network Exchange (ONNX)工具包；这些工具包不仅支撑导入几乎全部主流框架生成的ONNX模型，并且提供了基于CUDA与TensorRT的加速接口；同时适用于多种硬件环境。

External Library Calls: By utilizing the Python Node or NI's Python API (including NIPYAPI), LabVIEW supports accessing libraries within the Python ecosystem such as TensorFlow, PyTorch, and scikit-learn for tasks including model training, inference, and data processing. Similarly**, these AI models from MATLAB can also be integrated.

Natural Language Processing (NLP): Directly implementing complex NLP tasks in LabVIEW remains uncommon. However, external libraries, particularly Python-based ones, can be integrated into LabVIEW applications to enable NLP capabilities. Such as leveraging Python's NLP libraries for processing textual data to interpret user commands (enabling human-machine interaction). In robotics applications, especially within the domain of embodied AI, NLP facilitates more natural interactions and task execution by enhancing comprehension of human language.

2.2 AI Practices in LabVIEW Application Areas

AI technology is exhibiting its remarkable capabilities in LabVIEW's traditional areas, where it continues to demonstrate significant advantages.

Test and Measurement: AI可用于自动化测试流程、对海量测试数据进行分析、识别异常模式以及执行故障预测和诊断。例如，在实时分析传感器数据时应用机器学习算法以实现微秒级别实时参数提取（如高精度正弦参数拟合）。AI还可以优化测试系统本身：通过对待测设备参数与测试链之间关联性的分析来提高测试效率。

Industrial Automation and Control Systems: AI has become an integral component of industrial automation processes, underpinning intelligent decision-making capabilities and operational efficiency.

This section discusses the implementation of Edge AI inference using NI hardware platforms like CompactRIO when integrated with LabVIEW and AI-based capabilities. It enables real-time inference at the edge, including tasks such as product quality inspection, object recognition, and process monitoring on a production line.

Intelligent Control: Combining AI models developed within Python or MATLAB into industrial control systems utilizing LabVIEW allows for optimization of control strategies, enables adaptive control strategies, and permits predictive maintenance capabilities.

机器视觉：LabVIEW通过集成NI视觉或第三方AI视觉工具包，在工业机器视觉领域得到广泛应用，在表面缺陷检测、头盔安全检测以及异常物体识别等方面发挥重要作用，并显著提高生产效率和产品质量水平。

SCADA Systems: CompactRIO与LabVIEW结合，并借助人工智能分析能力，在工业过程中构建更智能的SCADA系统以实现监控、分析与优化的目的；例如，在机车测试系统中自动化地执行测试与性能分析任务

Embedded Systems: Selectively deploying AI models onto CompactRIO or similar embedded NI hardware is essential for establishing edge intelligence. However, these deployments are constrained by factors such as computing resources and power consumption. By employing techniques like model optimization (compression and quantization) along with hardware acceleration methods such as FPGA and NPU utilization, efficient AI inference can be reliably executed on embedded systems. LabVIEW's Embedded Development Module along with its toolchain provides robust support for deploying code across diverse processors. Additionally, it seamlessly integrates existing embedded code through the Inline C Node feature.

2.3 Methods for Integrating AI Models with LabVIEW

LabVIEW offers multiple flexible ways to integrate AI models:

Existing Toolkits: By employing NI's VDM and AIVT-OV, or alternative AI vision models/ONNX frameworks, supported models are seamlessly integrated into the system.

Calling External Libraries: By means of the Python Node, System Exec VI, or other language interfaces, AI models available in external environments such as Python (TensorFlow, PyTorch, scikit-learn), C++ (OpenCV), and MATLAB are accessible for training or inference purposes. This approach offers maximum flexibility, providing access to the latest AI algorithms and frameworks.

Deploying Models on NI Hardware: Trained models or those acquired through toolkits are deployable across a range of ni hardware platforms. These include compact rio and pxi processors, fpga targets such as accelerators like gpus, tpus, and npus. The ni vision development module is equipped with ip cores designed for use in processors and fpgas during the inference phase.

Cloud Integration: LabVIEW is increasingly integrating with leading cloud computing platforms such as AWS and Azure. Data is accessible on the cloud for purposes of model training or intricate analysis, with subsequent inference capabilities available both at local locations and on edge devices. National Instruments provides comprehensive AI and big data analytics platforms that are operational both locally and within the cloud, delivering actionable insights into OEM systems and machinery.

3. Theoretical Integration and Practical Implementation Considerations

Integrating AI models into LabVIEW's dataflow architecture faces both alignment issues and implementation hurdles.

3.1 Dataflow Programming and AI Model Execution

The fundamental aspect of LabVIEW revolves around dataflow programming, with program execution sequence dictated by data flow. AI models, particularly those involving deep learning, represent a process where input data traverses through network layers to yield outputs. Theoretically, an AI model can be conceptualized as a complex VI or subsystem with raw inputs such as images and sensor signals and outputs that constitute the processed results generated by AI techniques like classification labels, detection boxes, and predicted values.

In the LabVIEW block diagram, it can be depicted as data wires connected to a node that represents the AI model, passing through which data completes the computation. This mapping is straightforward and makes sense. However, practical implementation requires careful consideration of:

Data Format and Type Conversion: Highly efficient conversions are essential for maintaining precision without exception when translating between LabVIEW's data types and the formats used in AI frameworks such as NumPy arrays or TensorFlow tensors.

Parallelism and Concurrency: LabVIEW的数据流自然支持并行执行。如何利用这一特性以实现AI模型推理速度的优化尤其是针对多核处理器或FPGA的研究方向是一个重要研究领域例如在不同并行循环中分别分配不同的AI任务或者在FPGA上实现模型的并行计算

Within domains such as control systems and test-and-measurement practices, strict real-time constraints on response times are commonly encountered, particularly at the microsecond level. The integration of AI models into real-time systems demands that the inference process maintains system determinism without compromise. Deploying AI models onto real-time operating systems (RTOS) or Field-Programmable Gate Arrays (FPGA) is a common approach, requiring rigorous performance optimization and validation to ensure these implementations meet stringent timing constraints.

3.2 VI Architecture and AI Model Modularization

The LabVIEW virtual instrument (VI) architecture is geared towards promoting modularity by decomposing complex functions into reusable subVIs. By encapsulating AI models as individual VIs or collections thereof, developers can enhance code reusability and maintainability. For instance, an 'Object Detection VI' can be developed with an image input and outputs detailing detected objects along with their positions. This particular VI may internally leverage external Python scripts, NI toolkits, or third-party toolkits to execute the specific detection algorithm.

A modular design is conducive to establishing a well-organized system architecture, aiding in debugging and maintenance. Future research might focus on developing new LabVIEW abstractions or patterns to enhance the efficiency of AI workflow implementations, such as:

Model Version Control VI: For managing and handling different versions of AI models, this system provides a comprehensive solution for version control.

Online Update VI: Enabling real-time updates of AI models while ensuring continuous operation, without interrupting ongoing processes.

Model Monitoring VI: To track metrics including model inference speed and precision.

3.3 Performance Optimization

System performance plays a vital role in the integration of AI within LabVIEW, particularly for real-time processing and embedded systems.

Hardware Acceleration: Employing hardware accelerators such as GPUs, FPGAs, TPUs, and NPUs represents one of the most effective strategies for enhancing AI inference speed. These hardware types are supported by NI's AIVT-OV platform and third-party toolkit implementations. In embedded scenarios that demand small form factor, low power consumption, and high performance capabilities—FPGAs offer up to five times the performance improvement over embedded CPUs.

Model Enhancement: Enhancing the AI model itself, including techniques like quantization (converting floating-point weights to low-precision integers), pruning (eliminating insignificant connections), and distillation (training a more compact model using knowledge from a larger one), can effectively minimize the model's footprint while substantially boosting its operational efficiency. These optimizations not only reduce memory consumption but also improve computational speed, thereby facilitating its deployment on hardware with limited resources.

Algorithm Selection and Optimization: Selecting AI algorithms that are appropriately suited for specific tasks and hardware platforms. For instance, when dealing with object detection on edge devices, the YOLO series of models is typically favored over the Faster R-CNN approach due to their balanced performance between speed and accuracy. Thanks to algorithm optimization efforts, certain studies have shown that these models can run faster when implemented in LabVIEW compared to Python implementations. This proves particularly advantageous for industrial applications where efficiency is crucial.

Data Processing Efficiency: Optimised preprocessing and postprocessing of data are essential for achieving optimal performance. The LabVIEW platform offers an extensive collection of image and data processing functions designed to accelerate data handling prior to or following AI inference.

3.4 Specific Use Case Analysis

The research study highlighted a range of concrete application scenarios, offering valuable practical experience in the integration of LabVIEW and AI technologies:

Surface Defect Testing of Steel Balls: Employing NI Vision Assistant, the developed machine vision system enhances inspection efficiency.

Construction Site Safety Helmet Inspection and Object Surface Defect Assessment: Utilized intelligent vision systems.

Irregular Object Recognition: Achieved by combining CNN models.

The SCADA Locomotive Test System utilizes fully automated assessment and evaluation for performance analysis, implemented with CompactRIO and LabVIEW.

Through these cases, it is evident that the integration of LabVIEW with artificial intelligence technologies has the capability to address practical challenges within particular sectors. Moreover, this combination enhances the operational efficiency and advanced intelligence of systems.

4. Future Development Trends

The fusion of LabVIEW and AI is currently in a stage of swift advancement, which is expected to yield further advancements in the foreseeable future.

4.1 NI's Strategy and Roadmap (Next 1-5 Years)

NI has designated AI as a central strategic focus and is actively incorporating it into the LabVIEW platform.

Deep AI Integration and "Intelligent Test" Vision: NI aims to seamlessly integrate advanced AI technologies into current tools to build "intelligent testing" systems capable of sensing effectively while also learning continuously. These systems will adapt dynamically based on real-time data analysis. The vision encompasses three core components: instrumentation intelligence for device monitoring; user intelligence for personalized testing; and enterprise intelligence for organizational scalability.

The 'Engineer-in-the-Loop' AI Usage Principle stresses that AI should serve as a tool to enhance engineers' capabilities rather than supplant them. It is imperative for engineers to retain control over AI and employ it to boost efficiency and decision-making quality.

Industry-Ready AI: NI is dedicated in terms of data security and intellectual property protection, working closely with industry leaders to meet stringent demands of enterprise environments.

Toolkit and Feature Enhancements: *

Continuous Updates to AIVT-OV: The OpenVINO-based toolkit will undergo ongoing updates to cater for a broader range of modern models (including YOLOv5 and large language models), aiming at enhancing performance capabilities and incorporating state-of-the-art technologies such as the Object Tracking Track module and advanced scene segmentation techniques like SAM.

AI Assistant and Code Automation: NI has been developing an AI assistant to enhance LabVIEW Help documentation and VI descriptions, featuring code inheritance capability, automated note-taking features, and completion support, thereby optimizing productivity in the field of laboratory automation systems.

Stronger External Integration: LabVIEW aims to strengthen its integration with programming languages, including Python and .NET. By enabling the call to external AI libraries through multiple versions, it will continue to enhance integration capabilities.

The Graphical Programming Optimization initiative is dedicated to systematically enhancing the G language. It integrates additional graphical elements and intelligent prompts to make low-code/no-code development more accessible to a broader range of users, including individuals without extensive programming experience.

Enhanced Data Analysis Capabilities: Incorporate sophisticated algorithms and tools to facilitate the processing of vast amounts of data.

Edge Computing and Cloud Support: Enhance the AI-based deployment capabilities on edge computing devices (including compact hardware platforms like CompactRIO) while optimizing their integration with mainstream cloud services. Ensure seamless data synchronization between the device and the cloud platform, enabling efficient processing of tasks from the device to the cloud.

Placing greater emphasis on community ecosystems and third-party plugin development efforts, encouraging contributions from users to meet a broader range of industry requirements.

4.2 Impact of Emerging AI Technologies (Next 5+ Years)

Large Language Models (LLMs): The progression of Large Language Models (LLMs) offers promising opportunities for enhancing LabVIEW. Beyond NI's ongoing exploration of local deployment inference techniques, such as integration with DeepSeek, future advancements in LLM capabilities may enable a wider range of applications within the platform.

Natural Language Programming: Using natural language descriptions, this approach automatically generates LabVIEW code snippets and VI frameworks, thereby progressively reducing the learning curve for users.

智能化故障诊断: 借助大型语言模型（LLMs），对系统日志、错误信息以及用户描述进行详细分析，并提供更加精准且具操作性的故障排查策略。

Automated Report Generation: Automated generation of textual reports from test or monitoring data.

Smarter Human-Machine Interaction: Supporting advanced command management through voice commands or text input.

Embodied AI: Embodied AI combines diverse sensory data including language, vision, and hearing to enable adaptive behaviors in machines within the physical environment. LabVIEW offers a robust platform characterized by its capabilities in hardware interaction, real-time management, and comprehensive data acquisition from multiple sensors. Looking ahead, LabVIEW will facilitate the development of perception modules for data processing, decision-making units that integrate AI models for intelligent operations, and control systems for machine interactions.

Reinforcement Learning (RL): Reinforcement learning is particularly effective at solving challenging control and optimization tasks. In the future, reinforcement learning algorithms will be integrated into LabVIEW-based control systems to enable more intelligent and responsive adaptive strategies for applications like autonomous navigation tasks and process automation tasks.

4.3 Industry Transformation and Business Model Innovation

AI integrated with LabVIEW is expected to significantly enhance the advancement in test and measurement technology as well as industrial automation systems.

From the collection of raw data to deriving meaningful insights: The industry focus will transition from the collection of raw data to real-time analytics and actionable insights derived through AI-driven methods, thereby supporting informed decision-making processes.

Predictive Maintenance and Intelligent Diagnosis: AI models are utilized to predict equipment failures, allowing for proactive maintenance and the reduction of operational downtime costs.

Intelligent Manufacturing and Adaptive Systems: Constructing smart factories that are able to adapt to environmental changes and improve the efficiency of production processes.

New Business Models: *

Delivering AI-Driven Test and Analysis Services under the umbrella of TMaaS or AaaS.

Deploying pre-trained AI models that are customized for particular industrial uses, can be deployed on the LabVIEW platform.

Provide a range of consulting, software development, and system deployment services specifically designed for the integration of AI technologies with LabVIEW systems.

Creating and providing third-party toolkits and hardware acceleration solutions that enhance LabVIEW's AI capabilities.

These innovative business models would assist NI and its partners to become distinguished themselves in a competitive environment and grasp the growth opportunities that AI has made available.

5. Challenges and Countermeasures

Though prospective development is anticipated, the incorporation of LabVIEW with AI technology also encounters several obstacles.

Integration Complexity: The combination of diverse AI frameworks, model formats, and hardware platforms when integrated with LabVIEW demands a high level of technical proficiency.

Countermeasures: These toolkit distributions by NI and third-party vendors are making the process more straightforward. These enhancements to LabVIEW's open format support, such as ONNX, coupled with more intuitive interface designs for accessing external libraries, represent critical measures.

Data Processing and Management: AI models necessitate a substantial quantity of high-quality data for training. Efficient execution of large-scale data acquisition, preprocessing, labeling, and management within the LabVIEW environment presents a significant challenge.

Countermeasures: Optimize LabVIEW's data processing systems for enhanced functionality and ensure seamless integration with data management platforms and cloud storage. Investigate leveraging AI to automate data labeling processes and enhance the quality of datasets.

Performance Optimization and Real-Time Capability: Performing high-performance, deterministic AI inference on resource-constrained embedded hardware necessitates extensive optimization efforts.

Countermeasures: Maximize the use of hardware acceleration units, specifically Field-Programmable Gate Arrays (FPGAs) and Neural Processing Units (NPU). Create advanced tools for optimizing models to enhance performance. Investigate optimal strategies for integrating AI inference mechanisms into the LabVIEW Real-Time Operating System framework.

LabVIEW工程师技能转型：传统LabVIEW工程师需通过学习人工智能及相关领域（如Python编程、机器学习理论、数据科学）来适应新的需求。

Countermeasures: 系统性地提供培训材料和学习资源。鼓励跨学科的合作与交流。开发并分享更多与人工智能相关的LabVIEW实例和教程。

Model Interpretability and Reliability: For key mission applications, concerns regarding the 'non-transparent' nature and potential 'uncertainty' in AI models exist.

Countermeasures: Investigate techniques for enhancing AI model interpretability and incorporate these techniques into LabVIEW-based applications. Construct validation and monitoring frameworks to ensure the reliability of models in real-world operations.

Security: Deploying AI models into industrial systems and embedded devices needs to take into account model security aspects, including adversarial attacks, and data security.

Countermeasures: Adhere to industry standards for data security and privacy. Work closely with cybersecurity experts in protecting AI applications. NI is working on documenting software bills of materials and third-party dependencies in order to meet established security standards.

6. Business Models and Market Opportunities

The synergy between LabVIEW and AI enables innovative business models and valuable market opportunities for NI and its ecosystem.

AI-Enabled Hardware Products: Creating NI hardware platforms equipped with AIs integrated into the devices, whether they are designed to handle AI tasks efficiently (such as next-generation CompactRIO or PXI systems, specifically the Abrams Configureable Processing System (ACPS) family).

AI软件工具包和附加件: 提供更为强大的和易于操作的AI视觉、机器学习和深度学习工具包。

Industry-Specific AI Solutions: Tailored Development of AI Solutions leveraging the power of LabVIEW and AI specifically designed for the requirements of various industries, including automotive, aerospace, medical devices, and semiconductors.

AI Model Marketplace or Platform: Supporting developers by creating a platform where they can share and trade AI models that are readily deployable within LabVIEW environments.

AI Integration and Consulting Services: The company offers professional services designed to assist customers in integrating AI technology into their current LabVIEW systems.

Training and Certification: Providing professional training and certification programs for LabVIEW and AI integration.

Data Services: Integrating NI's data acquisition hardware to enable seamless integration from the initial data acquisition phase through comprehensive cloud storage solutions and advanced AI analytics capabilities.

These business models can assist NI and its partners to distinguish themselves within a competitive landscape and offer growth opportunities for AI-driven advancements.

7. Conclusion

The convergence of LabVIEW with artificial intelligence represents an inescapable trend across sectors such as test and measurement applications, industrial automation, control systems, and embedded technologies. By incorporating advanced technologies like machine learning, deep learning, computer vision, and natural language processing into the LabVIEW platform, it is possible to substantially elevate the intelligent capabilities of systems while effectively addressing intricate challenges that remain stubbornly resistant to conventional solutions.

Currently, LabVIEW has established a solid foundation for integrating AI through official and third-party toolkits. By leveraging flexible external library calls and ensuring hardware platform deployment support, it continues to lay the groundwork for enhanced AI capabilities. Looking ahead, NI's sustained focus on advancing AI technology will enable LabVIEW to further innovate through features like AI assistants, code automation capabilities, expanded toolkit offerings, and integration with cutting-edge innovations such as large language models (LLMs) and embodied AI systems. This strategic evolution will empower LabVIEW to become even smarter, more intuitive to use, and more powerful in its applications.

Despite encountering a range of challenges, including technological integration, performance optimization, and human resource development, these obstacles can be effectively managed through sustained technological innovation, collaborative ecosystem engagement, and engineer-specific skill improvement. The profound integration of LabVIEW and AI will catalyze the emergence of novel business models and create substantial market opportunities in areas such as industrial automation, preventive maintenance, and autonomous quality assurance. For LabVIEW engineers, integrating AI with their specialized knowledge is crucial for maintaining a competitive edge and expanding career advancement opportunities.

This detailed report, built upon the research and learning points provided, offers an in-depth examination of the integration of LabVIEW and AI. Future research can explore further aspects of this integration.

Robust technical solutions and performance assessment for determining deterministic AI inference within the CompactRIO real-time systems.

By leveraging LabVIEW's dataflow features, we can enhance the execution efficiency of specific AI models, such as CNN and Transformer.

Building prototypes for natural language-based control and automated report generation using LabVIEW and LLMs, but or in combination.

Architectural framework for LabVIEW in embedded AI perception and decision-making systems.

MLOps procedures for implementing LabVIEW AI applications encompass model deployment, monitoring processes, system updates, and version control mechanisms.

We expect the thorough analysis document to offer you access to useful resources and motivation.