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An implementation of **MLP** (Multi-Layer Perceptron) inference and training using DirectX 12 [Cooperative Vector][coop-vec-spec]. This library demonstrates GPU-accelerated neural network inference and training with cutting-edge shader features.
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An implementation of **MLP** (Multi-Layer Perceptron) inference and training using DirectX 12 [LinAlg Matrix][linalg-spec]. This library demonstrates GPU-accelerated neural network inference and training with cutting-edge shader features.
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- 🚀 **High Performance**: GPU-accelerated inference and training using [Cooperative Vector][coop-vec-overview]
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- 🚀 **High Performance**: GPU-accelerated inference and training using [LinAlg Matrix][linalg-overview]
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- 🔧 **Flexible Architecture**: Configurable layers, activations, and data types
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- 🎯 **Single-header HLSL**: Easy to integrate into any DX12 project
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## Requirements
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-**OS**: Windows 11 with [Developer Mode][win-dev-mode] enabled
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-**GPU**: Supports Shader Model 6.9 and Cooperative Vector in D3D12 (AMD Radeon™ RX 9000 Series GPUs or equivalent NVIDIA)
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-**GPU**: Supports Shader Model 6.10 and LinAlg Matrix in D3D12 (AMD Radeon™ RX 9000 Series GPUs or equivalent NVIDIA)
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-**Build**: CMake ≥ 3.21, Visual Studio 2022 (C++20), Windows SDK
-**Python**: Python 3.8+ with PyTorch (optional, for example python training)
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## Getting Started
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## DX12 Setup
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⚠️ **Important**: As of early 2026, Cooperative Vector requires experimental feature support.
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⚠️ **Important**: As of early 2026, LinAlg Matrix requires experimental feature support.
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1. Install a [Cooperative Vector supported driver][coop-vec-driver]
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1. Install a [LinAlg Matrix supported driver][linalg-driver]
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2. Enable [Experimental Shader Model][dx-experimental-shader-model] with [D3D12EnableExperimentalFeatures][dx-enable-experimental-features]**before** creating the device
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3. Compile shaders with **Shader Model 6.9**
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3. Compile shaders with **Shader Model 6.10**
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For a detailed walkthrough — including feature checks, weight matrix conversion (`GetLinearAlgebraMatrixConversionDestinationInfo` / `ConvertLinearAlgebraMatrix`), bias alignment, and full sample code — see the **[LinAlg Matrix MLP Guide](docs/linalg_matrix_mlp.md)**.
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