Learning to Compile Programs to Neural Networks

• URL: http://arxiv.org/abs/2407.15078v1
• Date: Sun, 21 Jul 2024 07:04:52 GMT
• Title: Learning to Compile Programs to Neural Networks
• Authors: Logan Weber, Jesse Michel, Alex Renda, Michael Carbin,
• Abstract summary: A $textitneural surrogate of a program$ is a neural network that mimics the behavior of a program. We present a technique for producing neural surrogates directly from program text without coupling neural surrogate generation and execution.
• Score: 10.203788801836385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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