Who is this for?
This advanced Learning Path is for engine developers who want to create neural graphics with ML Extensions for Vulkan.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Explain the purpose of neural graphics and the role of ML Extensions for Vulkan
- Set up the ML Emulation Layers for Vulkan to enable the extensions
- Run a sample Vulkan application that uses the extensions
- Debug the flow using RenderDoc
Prerequisites
Before starting, you will need the following:
- Windows 11 development machine
- Visual Studio 2022
- Visual Studio workload - Desktop development with C++
- Visual Studio workload - .NET desktop build tools
- Visual Studio Code
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
You’ll enable neural graphics workloads with ML Extensions for Vulkan by setting up the ML Emulation Layers and running Vulkan Samples. First, you’ll install development tools and configure the layers. You’ll build the Simple Tensor and Data Graph sample which runs a 2D average pooling operation through a data graph pipeline. Then, you’ll review the Scenario Runner assets for an inference workflow and use RenderDoc to capture frames and inspect Vulkan calls, shaders, tensors, and resource states.
Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
No. The ML Emulation Layers for Vulkan simulate these extensions so you can build and run the samples during development.
Run
build\windows\app\bin\Release\AMD64\vulkan_samples.exe sample simple_tensor_and_data_graph from the Vulkan-Samples repository. A new window opens and visualizes the operation.Run the Simple Tensor and Data Graph sample to execute a 2D average pooling operation through a data graph pipeline. Create input and output tensors, bind them with descriptor sets and pipeline layouts, and dispatch the SPIR-V-defined network.
The NSS model release includes a Windows-compatible Scenario Runner binary, the VGF model, and one input frame with expected output data. You can use these assets to run an end-to-end workflow and examine the VGF model.
Use RenderDoc when you need to diagnose unexpected visual output, examine Vulkan API calls, inspect resource states, or validate data graph pipeline execution. In Launch Application, set the Executable Path, Working Directory, and Command-line Arguments, select Launch, then press the F12 key while the sample is active. In the capture, inspect Vulkan API calls, shader inputs and outputs, and resource states.