Transformer Block

The transformer block, a fundamental building block in many deep learning models, aims to efficiently process sequential or spatial data by leveraging self-attention mechanisms to capture long-range dependencies. Current research focuses on optimizing transformer blocks for speed and efficiency, including hierarchical architectures (e.g., global-to-local modeling) and methods to reduce redundancy or simplify their design. These advancements are significantly impacting various fields, improving performance in tasks such as natural language processing, medical image analysis, and video compression by enabling faster inference and more accurate predictions with reduced computational costs.