Hybrid-Grained Pruning and Hardware Acceleration for Convolutional Neural Networks

Throughout various convolutional neural network (CNN) models, the sparsity increases as the network deepens, which poses significant potential to model compression and hardware acceleration. In this paper, a dual-factor hybridgrained pruning method is introduced to make a good balance between model compression and accuracy preservation. The proposed pruning method combines hardware-friendly unstructured vector-level pruning with structured filter-level pruning to explore multiple grains of sparsity in CNNs. The architecture of the corresponding hardware accelerator is then proposed based on the row-based convolution dataflow, which could fully utilize the hybrid sparsity to accelerate CNN processing. Experimental results demonstrate that the proposed method increases the compression rate by 1.08x while causing 0.21% accuracy loss compared to the state-of-the-art filter pruning method in VGG16, and 2.39% hardware resource increase compared to the accelerator without sparsity optimization.