CAI Neural API

Optimized for AVX/AVX2/AVX512 instruction sets and OpenCL, enabling fast neural network training and inference on CPUs and GPUs from AMD, Intel, and NVIDIA, as highlighted in its focus on high-performance computation.

Built in Pascal, the framework emphasizes code clarity and readability, making it easier for developers to understand and modify neural network implementations, per the project's philosophy.

Trained models can be saved and loaded across different hardware platforms (e.g., CPU to GPU), ensuring flexibility in deployment and reducing vendor lock-in, as mentioned in the features.

Includes a wide range of layers such as convolutional, pooling, normalization, and specialized ones like grouped pointwise convolutions, facilitating the creation of advanced neural architectures.

Enables implementation of modern networks like ResNet, DenseNet, MobileNet, GANs, and autoencoders, allowing for state-of-the-art model designs, as evidenced in the examples.