# Learning C to x86 Translation: An Experiment in Neural Compilation

arXiv: https://arxiv.org/abs/2108.07639 · HTML: https://mlsanity.com/paper/2108.07639/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Generative Language Modeling for Automated Theorem Proving](https://mlsanity.com/paper/2009.03393/) (2020)
- [Analysing Mathematical Reasoning Abilities of Neural Models](https://mlsanity.com/paper/1904.01557/) (2019)
- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [The Curious Case of Hallucinations in Neural Machine Translation](https://mlsanity.com/paper/2104.06683/) (2021)
- [An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation](https://mlsanity.com/paper/1812.08693/) (2018)
- [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018)
- [ProGraML: Graph-based Deep Learning for Program Optimization and Analysis](https://mlsanity.com/paper/2003.10536/) (2020)
- [On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networks](https://mlsanity.com/paper/1807.04015/) (2018)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Modeling Black-Box Components with Probabilistic Synthesis](https://mlsanity.com/paper/2010.04811/) (2020)
- [Program Language Translation Using a Grammar-Driven Tree-to-Tree Model](https://mlsanity.com/paper/1807.01784/) (2018)

## Built on by (most cited first, 12 of 12)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [Priority Sampling of Large Language Models for Compilers](https://mlsanity.com/paper/2402.18734/) (2024)
- [Learning Program Behavioral Models from Synthesized Input-Output Pairs](https://mlsanity.com/paper/2407.08597/) (2024)
- [ViC: Virtual Compiler Is All You Need For Assembly Code Search](https://mlsanity.com/paper/2408.06385/) (2024)
- [Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!](https://mlsanity.com/paper/2412.12163/) (2024)
- [QiMeng: Fully Automated Hardware and Software Design for Processor Chip](https://mlsanity.com/paper/2506.05007/) (2025)
- [QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code](https://mlsanity.com/paper/2511.01183/) (2025)
- [Exploring the Feasibility of End-to-End Large Language Model as a Compiler](https://mlsanity.com/paper/2511.04132/) (2025)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)

## Abstract (arXiv)

Deep learning has had a significant impact on many fields. Recently, code-to-code neural models have been used in code translation, code refinement and decompilation. However, the question of whether these models can automate compilation has yet to be investigated. In this work, we explore neural compilation, building and evaluating Transformer models that learn how to produce x86 assembler from C code. Although preliminary results are relatively weak, we make our data, models and code publicly available to encourage further research in this area.