The front-end optimization of embedded software has attracted a lot of interest in recent years. Since most computation is consumed in small fraction of code called hot path or comlxiVdtion kernel, even a bit performance improvement in hot path can result in great promotion, hence, limction inlining bcconic one of the effective techniques hccaiise taking away the fimction calling werhead will speed up thc hot path. Most of previous work inlines liinctions globally. and also causes unwanted code increment in cold path. In oiir approach to function inlining. Accurate Functions Dependency Graph (AFDG) is prexnted to accurately modeling the function calling behavior, and thc inlining optimization is carried out according to profiling data on AFDG, so that functions are partially inlined, and code increment in cold path is prevented. In order to get a satisfactory solution from the tremendous solution space in short time, a nieta-heuristic genetic algorithm (GA) is applied. In experiments, the proposed algorithm shows promising results compared with previous work,

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