Run Time Adaptive Network Slimming for Mobile Environments

Modern convolutional neural network (CNN) models offer significant performance improvement over previous methods, but suffer from high computational complexity and are not able to adapt to different run-time needs. To solve above problem, this paper proposes an inference-stage pruning method that offers multiple operation points in a single model, which can provide computational power-accuracy modulation during run time. This method can perform on shallow CNN models as well as very deep networks such as Resnet101. Experimental results show that up to 50% savings in the FLOP are available by trading away less than 10% of the top-1 accuracy.