Tensor Layout

Tensor layout optimization focuses on arranging data within tensors to maximize the efficiency of deep learning computations, particularly for large models like transformers and convolutional neural networks (CNNs). Current research emphasizes developing novel layouts and algorithms to minimize memory access and data movement, especially within resource-constrained environments like mobile devices and during model training. This optimization is crucial for accelerating deep learning inference and training, impacting the scalability and performance of various applications, from image processing to natural language processing. Efficient tensor layouts are key to unlocking the full potential of increasingly complex deep learning models.