Input Tensor

Input tensors are multi-dimensional arrays serving as fundamental data structures in various machine learning applications, particularly deep learning. Current research focuses on optimizing tensor processing for efficiency and scalability, addressing challenges like dynamic shapes, memory footprint reduction (e.g., through inverted activations), and efficient handling of large-scale datasets (e.g., via sequence parallelism). These advancements are crucial for improving the performance and applicability of deep learning models across diverse fields, including natural language processing, computer vision, and scientific computing, by enabling faster training and inference with reduced computational and memory demands.