  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.
