HRBP: Hardware-friendly Regrouping towards Block-wise Pruning for Sparse Training

Recently, pruning at initialization and training a sparse network from scratch (sparse training) become increasingly popular. However, most sparse training literature addresses only the unstructured sparsity, which in practice brings little benefit to the training acceleration on GPU due to the irregularity of non-zero weights. In this paper, we work on sparse training with fine-grained structured sparsity, by extracting a few dense blocks from unstructured sparse weights. For Convolutional Neural networks (CNN), however, the extracted dense blocks will be broken in backpropagation due to the shape transformation of convolution filters implemented by GEMM. Thus, previous block-wise pruning methods can only be used to accelerate the forward pass of sparse CNN training. To this end, we propose the Hardware-friendly Regrouping towards Block-based Pruning (HRBP), where the grouping is conducted on the kernel-wise mask. With HRBP, extracted dense blocks are preserved in backpropagation. We further propose HRBP++ to reduce zero kernels by extracting common sparse kernel patterns on all kernels within one block. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate that HRBP (HRBP++) can almost match the accuracy of unstructured sparse training methods while achieving a huge acceleration on hardware.