Tensor Slice

Tensor slicing involves partitioning high-dimensional data tensors into smaller, manageable slices for efficient processing, primarily addressing the computational challenges posed by massive datasets in machine learning. Current research focuses on optimizing tensor slicing strategies for parallel computation across multiple processors (e.g., GPUs, NPUs), particularly within large language model training and convolutional neural networks, aiming to minimize communication overhead and maximize computational efficiency. These advancements are crucial for scaling machine learning algorithms to handle increasingly large datasets and improve the performance of various applications, including natural language processing and image recognition.