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Releases: GPUOpen-LibrariesAndSDKs/MiniDXNN

v0.2.0

24 Apr 14:38

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MiniDXNN v0.2.0

MLP training support for MiniDXNN — GPU-accelerated forward and backward passes using DirectX 12 Cooperative Vector, with a C++ fallback path.

Features

  • HLSL MLP training (include/minidxnn/hlsl/mlp.hlsl): Forward and backward passes with mininn::forward() and mininn::backward()
  • Texture training example (example/02_texture_training): End-to-end MLP training on GPU with SGD, Adam, and Lion optimizers
  • C++ fallback (include/minidxnn/cpp/hlsl_compat.hpp): CPU execution path that compiles mlp.hlsl as C++ for environments without Cooperative Vector support

Changes from v0.1.0

  • Added MLP backward pass in HLSL
  • Added 02_texture_training example with GPU training pipeline
  • Added C++ fallback infrastructure (hlsl_compat.hpp)
  • Added training-related unit tests (atomic operations, MLP training)
  • Restructured HLSL header path from include/hlsl/ to include/minidxnn/hlsl/
  • Updated documentation and README

Requirements

  • Windows 11 with Developer Mode
  • GPU supports Shader Model 6.9 and Cooperative Vector in D3D12 (AMD Radeon™ RX 9000 Series GPUs or equivalent NVIDIA)
  • CMake 3.21+, Visual Studio 2022 (C++20)
  • Agility SDK 1.717.1-preview, DXC v1.8.2505.1
  • Python 3.8+ with PyTorch (optional, for reference training)

License

MIT — Copyright (c) 2026 Advanced Micro Devices, Inc.

v0.1.0

09 Apr 14:31

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MiniDXNN v0.1.0

Initial release of MiniDXNN — a header-only HLSL library for GPU-accelerated MLP inference using DirectX 12 Cooperative Vector.

Features

  • HLSL MLP inference (include/hlsl/mlp.hlsl): Configurable forward pass with mininn::forward()
  • Texture inference example: Train with PyTorch, export weights, run GPU inference

Requirements

  • Windows 10/11 with Developer Mode
  • GPU supports Shader Model 6.9 and Cooperative Vector in D3D12 (AMD Radeon™ RX 9000 Series GPUs or equivalent NVIDIA)
  • CMake 3.21+, Visual Studio 2022 (C++20)
  • Agility SDK 1.717.1-preview, DXC v1.8.2505.1

License

MIT — Copyright (c) 2026 Advanced Micro Devices, Inc.