SparseTrain

Abstract

Our community has improved the efficiency of deep learning applications by exploiting sparsity in inputs. Most of that work, though, is for inference, where weight sparsity is known statically, and/or for specialized hardware. In this paper, we propose SparseTrain, a software-only scheme to leverage dynamic sparsity during training on general-purpose SIMD processors. SparseTrain exploits zeros introduced by the ReLU activation function to both feature maps and their gradients. Exploiting such sparsity is challenging because the sparsity degree is moderate and the locations of zeros change over time.