Tensor Program

Tensor program optimization focuses on automatically generating efficient code for executing deep learning models on diverse hardware platforms, aiming to maximize performance and minimize development time. Current research emphasizes developing novel compiler techniques, including advanced auto-tuning strategies (like reinforcement learning and probabilistic programming), and efficient cost models to predict performance across different hardware and model architectures (e.g., transformers, ResNets). These advancements significantly impact the deployment of large-scale machine learning models by accelerating inference and training, ultimately enabling broader access to powerful AI applications.