Code Optimization

Code optimization aims to enhance software performance by improving efficiency and reducing resource consumption. Current research heavily utilizes machine learning, particularly large language models (LLMs) and reinforcement learning (RL), to automate this complex process, often focusing on iterative refinement or search-based approaches to discover optimal code transformations. These techniques are applied across various levels, from high-level program restructuring to low-level register transfer level (RTL) optimizations, showing promise in surpassing traditional compiler methods in specific scenarios. The resulting advancements have significant implications for improving software performance across diverse applications, from high-performance computing to embedded systems.