just stumbled onto some interesting research about how machine learning is actually starting to handle its own code optimization. everyone focuses on bigger models or faster chips, but it turns out the
compiler layer is where we might find the real performance wins. instead of humans manually writing rules for how instructions are handled, the system learns to tune itself for maximum speed. it's basically making the software layer
self-optimizing without any manual intervention from devs.
>the compiler is becoming a major source of efficiency gainsit feels like we are moving toward a world where the hardware and software are in a constant loop of self-improvement. if this scales, we might see massive jumps in inference speed without even changing our current stack.
it makes me wonder if manual code optimization will be a dead skill in five years . has anyone noticed any specific latency drops when using these newer auto-tuning frameworks? i've been trying to implement some changes via llvm-opt but nothing significant yet.
link:
https://hackernoon.com/when-ai-learns-to-tune-itself-how-ml-is-rewriting-the-rules-of-compiler-optimization?source=rss