NN is a C library for building and training neural networks, optimized for high performance on modern GPUs.
- Comprehensive layer support including convolutional, dense, activation, normalization, and resampling layers; efficient implementation of optimization algorithms like Adam; and advanced techniques like batch normalization and spectral normalization.
- Composite layer support including encoder-decoder and unified residual-in-residual dense blocks (URDDB) to simplify implementation.
- Tensor operations optimized for filling, copying, adding, mixing, scaling, normalizing and computing statistics.
- Experimental Lanczos resampling layer for both upsampling and downsampling, offering improved resampling compared to pooling or nearest neighbor methods. In some cases the Lanczos resampling layer may also replace strided convolutions or convolution transpose.
- Leveraging Vulkan compute for efficient utilization of computing cores, SIMD vectorization, and reduced memory bandwidth.
NN is currently in beta and under active development. Future releases will include additional layers, optimization algorithms, improved performance, documentation and examples.
The NN library was implemented by Jeff Boody under The MIT License.
Copyright (c) 2023 Jeff Boody
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