Learning to Compile Programs to Neural Networks
<|reference_start|>Learning to Compile Programs to Neural Networks: A $\textit{neural surrogate of a program}$ is a neural network that mimics the behavior of a program. Researchers have used these neural surrogates to automatically tune program inputs, adapt programs to new settings, and accelerate computations. Researchers traditionally develop neural surrogates by training on input-output examples from a single program. Alternatively, language models trained on a large dataset including many programs can consume program text, to act as a neural surrogate. Using a language model to both generate a surrogate and act as a surrogate, however, leading to a trade-off between resource consumption and accuracy. We present $\textit{neural surrogate compilation}$, a technique for producing neural surrogates directly from program text without coupling neural surrogate generation and execution. We implement neural surrogate compilers using hypernetworks trained on a dataset of C programs and find that they produce neural surrogates that are $1.9$-$9.5\times$ as data-efficient, produce visual results that are $1.0$-$1.3\times$ more similar to ground truth, and train in $4.3$-$7.3\times$ fewer epochs than neural surrogates trained from scratch.<|reference_end|>
@article{weber2024learning,
    title={Learning to Compile Programs to Neural Networks},
    author={Logan Weber, Jesse Michel, Alex Renda, Michael Carbin},
    journal={arXiv preprint arXiv:2407.15078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15078},
    primaryClass={cs.LG cs.AI}
}