Paper ID: 2409.11600

No Saved Kaleidosope: an 100% Jitted Neural Network Coding Language with Pythonic Syntax

Augusto Seben da Rosa, Marlon Daniel Angeli, Jorge Aikes Junior, Alef Iury Ferreira, Lucas Rafael Gris, Anderson da Silva Soares, Arnaldo Candido Junior, Frederico Santos de Oliveira, Gabriel Trevisan Damke, Rafael Teixeira Sousa

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: this https URL

Submitted: Sep 17, 2024