VESPA: Static profiling for binary optimization

Introduced VESPA (Vintage ESP Amended), a novel approach to static profiling for binary optimization. VESPA uses machine learning to infer branch instruction probabilities and derive code frequencies without requiring dynamic profiling, enabling binary optimizers like BOLT to improve performance for applications where dynamic profiling is prohibitive, such as end-user mobile applications. This research demonstrates a 6% speedup on top of highly optimized binaries built with Clang -O3.