Kernel Fusion

Kernel fusion is a technique aimed at improving efficiency and performance in various computational tasks, primarily by combining multiple operations into a single, optimized kernel. Current research focuses on applications in deep learning model training and inference, where it's used to reduce communication overhead and accelerate computations, as well as in explainable AI, enhancing the speed and accuracy of Shapley value calculations for feature importance analysis. These advancements have significant implications for accelerating large-scale machine learning and improving the interpretability of complex models, leading to faster training times and more reliable insights from data analysis.