Regarding deep learning for compilers, at Vathys we're using deep learning in compilers for deep learning code and it works well, achieving pretty good results

Regarding deep learning for compilers, at Vathys we're using deep learning in compilers for deep learning code and it works well, achieving pretty good results even compared to human implementations. But it's not that much of a gain and it can often get stuck on some code, especially if it's sufficiently new code. Just as surprisingly, genetic algorithms are almost as good but faster and more robust. Finally, I think people point to examples of much faster hardware accelerated workloads and optimized assembly code that share these characteristics: 1. Are relatively embarrassingly parallel 2. Are relatively control flow light 3. Generally tend to exhibit high compute/bandwith ratios. Hence, you see deep learning hardware, bitcoin mining hardware, optimized compression codec assembly, etc. pointed to as examples of acceleration, but this doesn't apply to all workloads, e.g. a web app.