tiny cuda neural networks

We have hosted the application tiny cuda neural networks in order to run this application in our online workstations with Wine or directly.

Quick description about tiny cuda neural networks:

This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. It will likely only work on an RTX 3090, an RTX 2080 Ti, or high-end enterprise GPUs. Lower-end cards must reduce the n_neurons parameter or use the CutlassMLP (better compatibility but slower) instead. tiny-cuda-nn comes with a PyTorch extension that allows using the fast MLPs and input encodings from within a Python context. These bindings can be significantly faster than full Python implementations; in particular for the multiresolution hash encoding.

Features:

• Tiny CUDA neural networks have a simple C++/CUDA API
• Learn a 2D image
• Requires an NVIDIA GPU
• Requires Windows: Visual Studio 2019
• Requires Linux: GCC/G++ 7.5 or higher
• Requires CUDA v10.2 or higher and CMake v3.21 or higher.

Programming Language: C++.
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