The explosive computation and memory requirements of convolutional neural networks (CNNs) hinder their deployment in resource-constrained devices. Because conventional CNNs perform identical parallelized computations even on redundant pixels, the saliency of various features in an image should be reflected for higher energy efficiency and market penetration. This paper proposes a novel channel and spatial gating network (CSGN) for adaptively selecting vital channels and generating spatial-wise execution masks. A CSGN can be characterized as a dynamic channel and a spatial-aware gating module by maximally utilizing opportunistic sparsity. Extensive experiments were conducted on the CIFAR-10 and ImageNet datasets based on ResNet. The results revealed that, with the proposed architecture, the amount of multiply-accumulate (MAC) operations was reduced by 1.97–11.78× and 1.37–13.12× on CIFAR-10 and ImageNet, respectively, with negligible accuracy degradation in the inference stage compared with the baseline architectures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.