Branch Prediction

Branch prediction aims to anticipate the outcome of conditional instructions in computer programs and other sequential processes, thereby optimizing execution speed and efficiency. Current research focuses on leveraging machine learning, particularly deep neural networks and reinforcement learning, to improve prediction accuracy, often employing techniques like graph neural networks for complex data structures or adapting models from other domains (e.g., language models for translation). These advancements have implications for diverse fields, including compiler optimization, accelerating scientific simulations (e.g., phylogenetic inference), and enhancing real-time applications like simultaneous machine translation.