Memory access instructions with auto-addressing modes are prevalent in various Instruction Set Architectures (ISAs), yet their use in compilers remains limited. Existing methods address code optimization in one of two ways: they either focus on reducing code size, but are constrained to basic block-level optimizations and may not fully exploit architectural benefits, or they optimize loop performance, often neglecting the advantages of post-increment instructions and focusing primarily on innermost loops while leaving outer loops unoptimized.

To address these shortcomings and meet the needs of real-world Machine Learning (ML) applications, we introduce \textit{Postiz}, a novel post-increment loop optimization technique. \textit{Postiz} extends post-increment optimizations beyond traditional limits, incorporating enhancements for inner loops, cross-loop regions, and nested loop structures. Through a profitability analysis, \textit{Postiz} optimizes code judiciously, leveraging architectural advantages and reducing code size without compromising improvement made by other optimizations.

Our experiments show that \textit{Postiz} is effective, achieving an optimization coverage of $98.04%$ on \textit{MobileNet} and \textit{BERT} benchmarks. In comparison to default LLVM optimization, \textit{Postiz} generates approximately four times more post-increment instructions. Moreover, it reduces code size by an average of $9.45%$ across various platforms. These improvements represent significant advancements over current methods, showcasing \textit{Postiz}’s potential to enhance compiler optimizations in a meaningful way.