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align-items: center; gap: 16px;\"\u003e\n  \u003cimg src=\"./img/sota-png.png\" width=\"370\"/\u003e\n  \u003ch2 style=\"margin: 0;\"\u003eSOTA – State Of The Art\u003c/h2\u003e\n\u003c/div\u003e\n\n**SOTA (State-Of-The-Art)** is the unified framework designed to extend **LabVIEW** with advanced **AI** and **high-performance computing** capabilities.  \nIt provides a **graph-oriented execution environment** that links LabVIEW with **ONNX Runtime** and multiple hardware accelerators such as CUDA, TensorRT, DirectML, OpenVINO, and OneDNN.\n\nSOTA enables engineers and researchers to:\n- Design and deploy neural networks or complex data pipelines directly inside LabVIEW  \n- Execute models efficiently across CPUs, GPUs, NPUs, FPGAs, or cloud platforms  \n- Integrate AI seamlessly into industrial and test-measurement systems  \n\n**Documentation:**\n- [Introduction to SOTA](https://graiphic.io/documentation/introduction-sota/)\n- [Installation Guide](https://graiphic.io/documentation/sota-installation/)\n- [Licensing](https://graiphic.io/documentation/licensing/)\n\n---\n\n\u003c!-- Accelerator --\u003e\n\u003cdiv style=\"display: flex; align-items: center; gap: 16px;\"\u003e\n  \u003cimg src=\"./img/accelerator.png\" width=\"70\"/\u003e\n  \u003ch2 style=\"margin: 0;\"\u003eAccelerator Toolkit\u003c/h2\u003e\n\u003c/div\u003e\n\nThe **LabVIEW Accelerator Toolkit** is the first ONNX-based computing framework for LabVIEW.  \nIt connects LabVIEW applications to the ONNX Runtime for hardware-accelerated data processing.\n\nMain highlights:\n- Built on **ONNX** and **ONNX Runtime**\n- Supports **CPU**, **GPU**, and **DirectML** execution\n- Enables high-performance AI graph deployment directly in LabVIEW\n\n**Documentation:**\n- [Installation Guide](https://graiphic.io/documentation/accelerator/quick-start/installation-guide)\n- [Beginner’s Guide](https://graiphic.io/documentation/accelerator/quick-start/general/beginners-guide/)\n- [Examples Guide](https://graiphic.io/documentation/accelerator/quick-start/general/examples-guide/)\n- [Troubleshooting](https://graiphic.io/documentation/accelerator/quick-start/general/troubleshooting/)\n- [Deployment](https://graiphic.io/documentation/accelerator/quick-start/general/deployment/)\n- [Hardware Compatibility](https://graiphic.io/documentation/accelerator/quick-start/general/hardware-compatibility/)\n- [FAQ](https://graiphic.io/documentation/accelerator/quick-start/general/faq/)\n- [Introduction](https://graiphic.io/documentation/accelerator/quick-start/general/introduction/)\n\n---\n\n\u003c!-- Deep Learning --\u003e\n\u003cdiv style=\"display: flex; align-items: center; gap: 16px;\"\u003e\n  \u003cimg src=\"./img/deeplearning.svg\" width=\"70\"/\u003e\n  \u003ch2 style=\"margin: 0;\"\u003eDeep Learning Toolkit\u003c/h2\u003e\n\u003c/div\u003e\n\nThe **LabVIEW Deep Learning Toolkit** provides native tools for neural-network creation, training, and inference inside LabVIEW.  \nIt is fully compatible with ONNX and shares the same execution backend as Accelerator.\n\nMain features:\n- Native **neural network design and training** inside LabVIEW  \n- **ONNX Runtime** integration for multi-hardware deployment  \n- Unified workflow with **SOTA** and **Accelerator**\n\n**Documentation:**\n- [Installation Guide](https://graiphic.io/documentation/deep-learning/)\n- [Architecture Overview](https://graiphic.io/documentation/deep-learning/)\n- [General Documentation](https://graiphic.io/documentation/introduction/)\n- [Beginner’s Guide](https://graiphic.io/documentation/beginners-guide/)\n- [Examples Guide](https://graiphic.io/documentation/examples-guide/)\n- [Troubleshooting](https://graiphic.io/documentation/troubleshooting/)\n- [Deployment](https://graiphic.io/documentation/deployment/)\n- [FAQ](https://graiphic.io/documentation/faq/)\n\n---\n\n© 2025 **Graiphic Technologies de France**  \n[https://graiphic.io](https://graiphic.io)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgraiphic%2Fgraiphic-documentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgraiphic%2Fgraiphic-documentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgraiphic%2Fgraiphic-documentation/lists"}