  The key to performance optimization of a program is to decide correctly when
a certain transformation should be applied by a compiler. This is an ideal
opportunity to apply machine-learning models to speed up the tuning process;
while this realization has been around since the late 90s, only recent
advancements in ML enabled a practical application of ML to compilers as an
end-to-end framework.
  This paper presents ACPO: \textbf{\underline{A}}I-Enabled
\textbf{\underline{C}}ompiler-driven \textbf{\underline{P}}rogram
\textbf{\underline{O}}ptimization; a novel framework to provide LLVM with
simple and comprehensive tools to benefit from employing ML models for
different optimization passes. We first showcase the high-level view, class
hierarchy, and functionalities of ACPO and subsequently, demonstrate a couple
of use cases of ACPO by ML-enabling the Loop Unroll and Function Inlining
passes and describe how ACPO can be leveraged to optimize other passes.
Experimental results reveal that ACPO model for Loop Unroll is able to gain on
average 4\% compared to LLVM's O3 optimization when deployed on Polybench.
Furthermore, by adding the Inliner model as well, ACPO is able to provide up to
4.5\% and 2.4\% on Polybench and Cbench compared with LLVM's O3 optimization,
respectively.
