Paper ID: 2302.04852

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh

We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware.

Submitted: Feb 9, 2023