Research on efficient training of transformer model based on sparse matrix optimization

The Transformer model, with its powerful self-attention mechanism and parallel computing capability, has been widely used in natural language processing and computer vision. However, its high computational complexity and memory consumption have limited its application in large-scale datasets and resource-constrained environments. This study proposes an efficient training method for Transformer models based on sparse matrix optimization. By designing sparse matrix patterns and incorporating dynamic sparsity adjustment strategies, the method significantly reduces computational complexity and resource requirements during training while maintaining model performance. Experiments conducted on various tasks, such as machine translation and image classification, demonstrate that the proposed method greatly reduces training time and memory usage with minimal performance loss. This research not only offers new insights into the efficient optimization of Transformer models but also expands the potential applications of sparse matrix technology in deep learning.