Welcome to the official documentation index for Graiphic Toolkits for LabVIEW.
Below you will find direct access to the online documentation for SOTA, Accelerator, and Deep Learning 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
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