We developed a jitted compiler for training Artificial Neural Networks using C++, LLVM and Cuda. It features object-oriented characteristics, strong typing, parallel workers for data pre-processing, pythonic syntax for expressions, PyTorch like model declaration and Automatic Differentiation. We implement the mechanisms of cache and pooling in order to manage VRAM, cuBLAS for high performance matrix multiplication and cuDNN for convolutional layers. Our experiments with Residual Convolutional Neural Networks on ImageNet, we reach similar speed but degraded performance. Also, the GRU network experiments show similar accuracy, but our compiler have degraded speed in that task. However, our compiler demonstrates promising results at the CIFAR-10 benchmark, in which we reach the same performance and about the same speed as PyTorch. We make the code publicly available at: https://github.com/NoSavedDATA/NoSavedKaleidoscope

本研究解决了训练人工神经网络的编译效率与性能问题，提出了一种基于C++、LLVM和Cuda的Jitted编译器，具备面向对象特点和Python风格的语法。实验结果表明，在CIFAR-10基准测试中，该编译器在性能和速度上与PyTorch相当，显示出良好的应用潜力。

无保存万花筒：一种具有Python语法的100% Jitted神经网络编码语言