I would love to be able to use Julia to design embedable control systems and signal processing architectures. Since Julia can’t cross compile to embedable DSPs or SoCs and may not have the stability that critical systems may need, perhaps it’s better to convert the Julia to clang or c or something else.
I was wondering if some combination of LLM transformer could be trained by a neural network that generatively learned how to convert Julia syntax to clang syntax by using the llvm IR as a reference of goodness.
I asked ChatGPT about this and it seemed hopeful. What do you think? Here is the response.
Yes, it’s theoretically possible to train a neural network to convert code from one LLVM-compatible language to another by going through the LLVM Intermediate Representation (IR). Here’s a basic outline of how this could work:
- Understanding LLVM IR: LLVM IR serves as a middle ground, allowing different languages to be compiled into a common format before being further compiled into machine code. It’s designed to be a sort of universal assembly language, which makes it a good intermediary.
- Training Data: To train such a neural network, you would need a large dataset of code examples in both source languages (e.g., Julia and C) that compile to the same or very similar LLVM IR. This dataset would be used to teach the neural network the patterns of how different constructs in one language translate into constructs in the other language through LLVM IR.
- Model Architecture: The model architecture could take several forms, but sequence-to-sequence models or transformers, which have been successful in natural language processing tasks, might be particularly well-suited. These architectures can handle the complexity of code translation by considering the entire sequence of code when making translations.
- Training Process: During training, the neural network would learn to predict the target language code given source language code, optimizing for translations that compile down to the same or functionally equivalent LLVM IR. This process involves a lot of trial and error, tweaking the model architecture, and hyperparameters to improve performance.
- Challenges: One of the main challenges is ensuring that the translated code not only compiles to the same LLVM IR but also preserves the semantic intent of the original code. Another challenge is handling the vast diversity of code patterns and libraries across languages.
While the idea is theoretically feasible and very intriguing, it’s a complex challenge that would require a significant amount of specialized data and computational resources. Additionally, the practical applications would be fascinating, potentially streamlining cross-language development and integration tasks.
