Welcome to the official documentation index for Graiphic Toolkits for LabVIEW.
Below you will find direct access to the online documentation for SOTA, Accelerator, Deep Learning Toolkit, Computer Vision Toolkit, and CUDA Toolkit.
SOTA (State-Of-The-Art) is the unified framework designed to extend LabVIEW with advanced AI and high-performance computing capabilities.
It provides a graph-oriented execution environment that links LabVIEW with ONNX Runtime and multiple hardware accelerators such as CUDA, TensorRT, DirectML, OpenVINO, and OneDNN.
SOTA enables engineers and researchers to:
- Design and deploy neural networks or complex data pipelines directly inside LabVIEW
- Execute models efficiently across CPUs, GPUs, NPUs, FPGAs, or cloud platforms
- Integrate AI seamlessly into industrial and test-measurement systems
Documentation:
The LabVIEW Accelerator Toolkit is the first ONNX-based computing framework for LabVIEW.
It connects LabVIEW applications to the ONNX Runtime for hardware-accelerated data processing.
Main highlights:
- Built on ONNX and ONNX Runtime
- Supports CPU, GPU, and DirectML execution
- Enables high-performance AI graph deployment directly in LabVIEW
Documentation:
- Installation Guide
- Beginner’s Guide
- Examples Guide
- Troubleshooting
- Deployment
- Hardware Compatibility
- FAQ
- Introduction
The LabVIEW Deep Learning Toolkit provides native tools for neural-network creation, training, and inference inside LabVIEW.
It is fully compatible with ONNX and shares the same execution backend as Accelerator.
Main features:
- Native neural network design and training inside LabVIEW
- ONNX Runtime integration for multi-hardware deployment
- Unified workflow with SOTA and Accelerator
Documentation:
- Installation Guide
- Architecture Overview
- General Documentation
- Beginner’s Guide
- Examples Guide
- Troubleshooting
- Deployment
- FAQ
The Computer Vision Toolkit brings modern vision-AI capabilities into LabVIEW using the same unified ONNX Runtime backend as SOTA and Accelerator.
It enables real-time processing, image pipelines, and deployment of vision models for industrial and robotics applications.
Main features:
- Full ONNX vision model support
- Real-time image processing pipelines
- Integration with SOTA, Accelerator and Deep Learning Toolkit
- Easy deployment on CPUs, GPUs, and edge devices
Documentation:
The CUDA Toolkit for LabVIEW enables direct GPU acceleration for AI models, numerical computation, and parallel processing.
It integrates seamlessly with SOTA, Accelerator and Deep Learning Toolkit to bring maximum GPU performance to LabVIEW applications.
Main features:
- Native CUDA execution backend
- Automatic GPU memory management
- ONNX Runtime CUDA EP integration
- Compatible with NVIDIA RTX, Jetson, and data-center GPUs
Documentation:
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