Given the complexity of modern compiler optimizations, integrating a small neural network into a C compiler like GCC might help generate faster executable code by guiding optimization decisions.
V2 will be wasm and then everyone will be out of a job. You're welcome.
Main problem of C optimization, C is not expressive language, and using it tends to quantize problem to tiny chunks, so lost overall picture.
So yes, probably LLVM approach with optimizing already coded something similar to bytecode, could got some speedup, but with using higher level languages, speedup could be magnitudes better.
I think, future belongs to some hybrid approaches, good known domains create, or better word - handcraft with C or even on Assembly, but for less known use something like Prolog solver.
What makes neural networks and learning irrelevant is because there aren't a billion parameters, most parameters don't depend on each other. It is not a search problem. You have the wrong domain.
This is like saying "hey let's crack that password using neural networks, stockfish is so advanced!"
And people have tried auto searching parameters, the results were as I described in my response. Moreover, imagine releasing code and having cluebies complain about trying to make it go faster with -fomit-frame-pointer. The whole idea is wasteful and cringe.