Auto Tuning

Auto-tuning focuses on automating the optimization of system parameters to achieve optimal performance, addressing the time-consuming and resource-intensive nature of manual tuning. Current research emphasizes data-driven approaches, employing machine learning techniques like deep reinforcement learning, Bayesian optimization, and neural networks (including neural ODEs and variational autoencoders) to efficiently explore high-dimensional parameter spaces. These advancements are significantly impacting diverse fields, from automotive thermal management and robotics to database optimization and high-performance computing, by accelerating development cycles and improving system efficiency.