Chapter 03.05: Efficient Transformers
Efficient Transformers are designed to mitigate the computational and memory requirements of standard transformer architectures, particularly when dealing with large-scale datasets or resource-constrained environments. They aim to address issues such as scalability and efficiency in training and inference. One approach used in efficient transformers is replacing the standard self-attention mechanism with more lightweight attention mechanisms, which reduce the computational complexity of attending to long sequences by approximating the attention mechanism with lower-rank matrices or restricting attention to local or sparse regions of the sequence. These approaches enable transformers to be more practical for real-world applications where computational resources are limited.