Butterfly Matrix

Butterfly matrices are structured, sparse matrices used to create more efficient and parameter-light neural network architectures. Current research focuses on applying these matrices within various model types, including transformers and normalizing flows, to improve training speed and reduce memory requirements for large language models and other deep learning applications. This approach addresses the high computational cost of training large models by leveraging the inherent structure of butterfly matrices to achieve comparable performance with fewer parameters and faster training times. The resulting efficiency gains have significant implications for both the scalability of deep learning research and the deployment of large models in resource-constrained environments.