The computational and memory requirements of Deep Learning (DL) models pose challenges, especially over long training periods. Sparse training offers a potential solution for such computational burdens. While libraries such as cuSparse and cuSparseLt provide implementations of sparse routines necessary for frameworks such as Pytorch, achieving substantial speedups, especially for extreme sparsity ratios, remains difficult. Sparse training exploits the inherent sparsity of neural networks and relies on sparse matrix operations such as Sparse Matrix-Matrix Multiplication … Continue reading ESPLAG